Read the Docs build information
Build id: 1119369
Project: datarobot-public-api-client
Version: latest-release
Commit: 13f723fd0831b5c9b66eb71a9c2d49ce79c74861
Date: 2022-09-29T16:22:05.444863Z
State: finished
Success: True


[rtd-command-info] start-time: 2022-09-29T16:22:07.568021Z, end-time: 2022-09-29T16:22:12.748661Z, duration: 5, exit-code: 0
git clone --no-single-branch --depth 50 git@github.com:datarobot/public_api_client.git .
Cloning into '.'...
Warning: Permanently added 'github.com,192.30.255.112' (ECDSA) to the list of known hosts.

[rtd-command-info] start-time: 2022-09-29T16:22:12.941776Z, end-time: 2022-09-29T16:22:13.151155Z, duration: 0, exit-code: 0
git checkout --force 13f723fd0831b5c9b66eb71a9c2d49ce79c74861
Note: switching to '13f723fd0831b5c9b66eb71a9c2d49ce79c74861'.

You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by switching back to a branch.

If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -c with the switch command. Example:

  git switch -c <new-branch-name>

Or undo this operation with:

  git switch -

Turn off this advice by setting config variable advice.detachedHead to false

HEAD is now at 13f723fd [DSX-2352] 2.28.2b0 -> 2.28.2 (#1945)

[rtd-command-info] start-time: 2022-09-29T16:22:13.344039Z, end-time: 2022-09-29T16:22:13.409426Z, duration: 0, exit-code: 0
git clean -d -f -f


[rtd-command-info] start-time: 2022-09-29T16:22:14.544968Z, end-time: 2022-09-29T16:22:19.098674Z, duration: 4, exit-code: 0
python3.7 -mvirtualenv 
Using base prefix '/home/docs/.pyenv/versions/3.7.9'
New python executable in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/bin/python3.7
Also creating executable in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/bin/python
Installing setuptools, pip, wheel...
done.

[rtd-command-info] start-time: 2022-09-29T16:22:19.328187Z, end-time: 2022-09-29T16:22:21.071716Z, duration: 1, exit-code: 0
python -m pip install --upgrade --no-cache-dir pip setuptools<58.3.0
Requirement already satisfied: pip in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages (22.2.2)
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Installing collected packages: setuptools
  Attempting uninstall: setuptools
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Successfully installed setuptools-58.2.0

[rtd-command-info] start-time: 2022-09-29T16:22:21.326275Z, end-time: 2022-09-29T16:22:27.753793Z, duration: 6, exit-code: 0
python -m pip install --upgrade --no-cache-dir pillow==5.4.1 mock==1.0.1 alabaster>=0.7,<0.8,!=0.7.5 commonmark==0.9.1 recommonmark==0.5.0 sphinx<2 sphinx-rtd-theme<0.5 readthedocs-sphinx-ext<2.2 jinja2<3.1.0
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Building wheels for collected packages: mock
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Successfully built mock
Installing collected packages: snowballstemmer, pytz, mock, commonmark, alabaster, urllib3, sphinxcontrib-serializinghtml, six, pyparsing, Pygments, pillow, MarkupSafe, imagesize, idna, docutils, charset-normalizer, certifi, babel, sphinxcontrib-websupport, requests, packaging, jinja2, sphinx, readthedocs-sphinx-ext, sphinx-rtd-theme, recommonmark
Successfully installed MarkupSafe-2.1.1 Pygments-2.13.0 alabaster-0.7.12 babel-2.10.3 certifi-2022.9.24 charset-normalizer-2.1.1 commonmark-0.9.1 docutils-0.17.1 idna-3.4 imagesize-1.4.1 jinja2-3.0.3 mock-1.0.1 packaging-21.3 pillow-5.4.1 pyparsing-3.0.9 pytz-2022.2.1 readthedocs-sphinx-ext-2.1.9 recommonmark-0.5.0 requests-2.28.1 six-1.16.0 snowballstemmer-2.2.0 sphinx-1.8.6 sphinx-rtd-theme-0.4.3 sphinxcontrib-serializinghtml-1.1.5 sphinxcontrib-websupport-1.2.4 urllib3-1.26.12

[rtd-command-info] start-time: 2022-09-29T16:22:27.944164Z, end-time: 2022-09-29T16:23:11.265690Z, duration: 43, exit-code: 0
python -m pip install --upgrade --upgrade-strategy eager --no-cache-dir .[dev]
Processing /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release
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Building wheels for collected packages: tornado, datarobot, atomicwrites, jupyter-latex-envs
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Installing collected packages: webencodings, wcwidth, trafaret, Send2Trash, pyflakes, pycodestyle, ptyprocess, pickleshare, mypy-extensions, mistune, mccabe, jupyter-highlight-selected-word, ipython-genutils, fastjsonschema, distlib, cookies, backcall, zipp, typing-extensions, typed-ast, traitlets, tornado, tomli, toml, testpath, setuptools, pyzmq, pyyaml, python-dateutil, pyrsistent, pycparser, py, psutil, prompt-toolkit, prometheus-client, platformdirs, Pillow, pexpect, pathspec, parso, pandocfilters, numpy, nest-asyncio, more-itertools, mock, lxml, isort, flake8, filelock, entrypoints, docutils, decorator, debugpy, coverage, contextlib2, bleach, attrs, atomicwrites, terminado, Sphinx, responses, requests-toolbelt, pandas, matplotlib-inline, jupyter-core, jedi, importlib-resources, importlib-metadata, cffi, virtualenv, sphinx-rtd-theme, pluggy, numpydoc, jupyter-client, jsonschema, ipython, datarobot, click, argon2-cffi-bindings, tox, pytest, nbformat, ipykernel, black, argon2-cffi, pytest-cov, nbconvert, notebook, nbsphinx, jupyter-latex-envs, jupyter-contrib-core, jupyter-nbextensions-configurator, jupyter-contrib-nbextensions
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    Uninstalling sphinx-rtd-theme-0.4.3:
      Successfully uninstalled sphinx-rtd-theme-0.4.3
Successfully installed Pillow-6.2.2 Send2Trash-1.8.0 Sphinx-1.8.3 argon2-cffi-21.3.0 argon2-cffi-bindings-21.2.0 atomicwrites-1.4.1 attrs-22.1.0 backcall-0.2.0 black-21.12b0 bleach-5.0.1 cffi-1.15.1 click-8.0.4 contextlib2-21.6.0 cookies-2.2.1 coverage-6.4.4 datarobot-2.28.2 debugpy-1.6.3 decorator-5.1.1 distlib-0.3.6 docutils-0.15.2 entrypoints-0.4 fastjsonschema-2.16.2 filelock-3.8.0 flake8-2.6.2 importlib-metadata-4.12.0 importlib-resources-5.9.0 ipykernel-6.10.0 ipython-7.34.0 ipython-genutils-0.2.0 isort-5.8.0 jedi-0.18.1 jsonschema-4.3.1 jupyter-client-7.2.0 jupyter-contrib-core-0.4.0 jupyter-contrib-nbextensions-0.5.1 jupyter-core-4.11.1 jupyter-highlight-selected-word-0.2.0 jupyter-latex-envs-1.4.6 jupyter-nbextensions-configurator-0.5.0 lxml-4.9.1 matplotlib-inline-0.1.6 mccabe-0.5.3 mistune-0.8.4 mock-3.0.5 more-itertools-8.14.0 mypy-extensions-0.4.3 nbconvert-5.3.1 nbformat-5.6.1 nbsphinx-0.8.9 nest-asyncio-1.5.5 notebook-6.1.6 numpy-1.21.6 numpydoc-1.2.1 pandas-1.3.5 pandocfilters-1.5.0 parso-0.8.3 pathspec-0.10.1 pexpect-4.8.0 pickleshare-0.7.5 platformdirs-2.5.2 pluggy-0.13.1 prometheus-client-0.14.1 prompt-toolkit-3.0.31 psutil-5.9.2 ptyprocess-0.7.0 py-1.11.0 pycodestyle-2.0.0 pycparser-2.21 pyflakes-1.2.3 pyrsistent-0.18.1 pytest-4.6.11 pytest-cov-4.0.0 python-dateutil-2.8.2 pyyaml-6.0 pyzmq-24.0.1 requests-toolbelt-0.9.1 responses-0.9.0 setuptools-65.4.0 sphinx-rtd-theme-0.1.9 terminado-0.13.3 testpath-0.6.0 toml-0.10.2 tomli-1.2.3 tornado-5.1.1 tox-3.25.1 trafaret-2.1.1 traitlets-5.4.0 typed-ast-1.5.4 typing-extensions-4.3.0 virtualenv-20.16.5 wcwidth-0.2.5 webencodings-0.5.1 zipp-3.8.1

[rtd-command-info] start-time: 2022-09-29T16:23:13.830190Z, end-time: 2022-09-29T16:23:13.893440Z, duration: 0, exit-code: 0
cat docs/conf.py
# -*- coding: utf-8 -*-
#
# Copyright 2021 DataRobot, Inc. and its affiliates.
#
# All rights reserved.
#
# DataRobot, Inc.
#
# This is proprietary source code of DataRobot, Inc. and its
# affiliates.
#
# Released under the terms of DataRobot Tool and Utility Agreement.
#
# DataRobot Python API documentation build configuration file, created by
# sphinx-quickstart on Mon Mar 16 10:50:19 2015.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.

import os
import shutil
import sys
from zipfile import ZipFile

sys.path.insert(0, os.path.abspath(".."))
from datarobot import __version__ as version  # noqa

# Make sure magic 'tags' field is defined.
# (Not actually useful but makes Cython and pylint errors go away.)
if "tags" not in locals():
    tags = None
if "b" in version:
    # mainline quarterly releases never have the beta "b" in the version, only internal & weekly do
    tags.add("include_experimental_docs")

# Copy the changelog to the docs directory so sphinx will include it
root_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
changelog_path = os.path.join(root_dir, "CHANGES.rst")
dest_path = os.path.join(root_dir, "docs", "CHANGES.rst")
assert os.path.isfile(changelog_path)
shutil.copyfile(changelog_path, dest_path)

# Create a zip file of the example resources (csv, py, ipynb)
# so that it can be included it as a download within the HTML docs
files_to_zip = []
for root, _, files in os.walk("examples"):
    basename = os.path.basename(root)
    # skip "hidden" directories like notebook autosaves and _build
    if not basename.startswith("_") and not basename.startswith("."):
        for file in files:
            name, ext = os.path.splitext(file)
            ext_lower = ext.lower()
            if ext_lower in [".csv", ".xls", ".xlsx", ".ipynb", ".py"]:
                files_to_zip.append(os.path.join(root, file))
with ZipFile("datarobot_examples.zip", "w") as examples_zip:
    for file in files_to_zip:
        examples_zip.write(file)

# -- General configuration -----------------------------------------------------

# If your documentation needs a minimal Sphinx version, state it here.
# needs_sphinx = '1.0'

# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
extensions = [
    "sphinx.ext.autodoc",
    "sphinx.ext.autosummary",
    "sphinx.ext.doctest",
    "numpydoc",
    "nbsphinx",
]

# make warnings go away- as advised by
# https://github.com/phn/pytpm/issues/3#issuecomment-12133978
numpydoc_show_class_members = False

nbsphinx_execute = "never"

# Add any paths that contain templates here, relative to this directory.
templates_path = ["_templates"]

# The suffix of source filenames.
source_suffix = ".rst"

# Showing automatically documented members in same order as they are in source
autodoc_member_order = "bysource"

# The encoding of source files.
# source_encoding = 'utf-8-sig'

# The master toctree document.
master_doc = "index"

# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# Thes are both the full X.Y.Z version, including alpha/beta/rc tags.
# We think Sphinx probably wants both to be defined.
release = version

project = u"DataRobot Python Client"
copyright = u"2019, DataRobot, Inc"


# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
# language = None

# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
# today = ''
# Else, today_fmt is used as the format for a strftime call.
# today_fmt = '%B %d, %Y'

# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = ["_build", "**/.ipynb_checkpoints"]

# The reST default role (used for this markup: `text`) to use for all documents.
# default_role = None

# If true, '()' will be appended to :func: etc. cross-reference text.
# add_function_parentheses = True

# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
# add_module_names = True

# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
# show_authors = False

# The name of the Pygments (syntax highlighting) style to use.
pygments_style = "sphinx"

# A list of ignored prefixes for module index sorting.
# modindex_common_prefix = []


# -- Options for HTML output ---------------------------------------------------

# The theme to use for HTML and HTML Help pages.  See the documentation for
# a list of builtin themes.
import sphinx_rtd_theme

html_theme = "sphinx_rtd_theme"
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]

# Theme options are theme-specific and customize the look and feel of a theme
# further.  For a list of options available for each theme, see the
# documentation.
# html_theme_options = {}

# Add any paths that contain custom themes here, relative to this directory.
# html_theme_path = []

# The name for this set of Sphinx documents.  If None, it defaults to
# "<project> v<release> documentation".
# html_title = None

# A shorter title for the navigation bar.  Default is the same as html_title.
# html_short_title = None

# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
# html_logo = None

# The name of an image file (within the static path) to use as favicon of the
# docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
# html_favicon = None

# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ["_static"]

# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
# html_last_updated_fmt = '%b %d, %Y'

# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
# html_use_smartypants = True

# Custom sidebar templates, maps document names to template names.
# html_sidebars = {}

# Additional templates that should be rendered to pages, maps page names to
# template names.
# html_additional_pages = {}

# If false, no module index is generated.
# html_domain_indices = True

# If false, no index is generated.
# html_use_index = True

# If true, the index is split into individual pages for each letter.
# html_split_index = False

# If true, links to the reST sources are added to the pages.
# html_show_sourcelink = True

# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
# html_show_sphinx = True

# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
# html_show_copyright = True

# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it.  The value of this option must be the
# base URL from which the finished HTML is served.
# html_use_opensearch = ''

# This is the file name suffix for HTML files (e.g. ".xhtml").
# html_file_suffix = None

# Output file base name for HTML help builder.
htmlhelp_basename = "DataRobotPythonDoc"


# -- Options for LaTeX output --------------------------------------------------

# latex_elements = {
#     The paper size ('letterpaper' or 'a4paper').
#     'papersize': 'letterpaper',

#     The font size ('10pt', '11pt' or '12pt').
#     'pointsize': '10pt',

#     Additional stuff for the LaTeX preamble.
#     'preamble': '',
# }

fh = open("latex_preamble.tex", "r+")
PREAMBLE = fh.read()
fh.close()
latex_elements = {
    "inputenc": "",
    "utf8extra": "",
    "preamble": PREAMBLE,
}

# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title, author, documentclass [howto/manual]).
latex_documents = [
    (
        "index",
        "DataRobotPython_{}_Docs.tex".format(version),
        u"DataRobot Python API Documentation",
        "DataRobot, Inc.",
        "manual",
    ),
]

# The name of an image file (relative to this directory) to place at the top of
# the title page.
# latex_logo = None

# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
# latex_use_parts = False

# If true, show page references after internal links.
# latex_show_pagerefs = False

# If true, show URL addresses after external links.
# latex_show_urls = False

# Documents to append as an appendix to all manuals.
# latex_appendices = []

# If false, no module index is generated.
# latex_domain_indices = True


# -- Options for manual page output --------------------------------------------

# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
    ("index", "datarobotpython", u"DataRobot Python API Documentation", [u"DataRobot, Inc."], 1)
]

# If true, show URL addresses after external links.
# man_show_urls = False


# -- Options for Texinfo output ------------------------------------------------

# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
#  dir menu entry, description, category)
texinfo_documents = [
    (
        "index",
        "DataRobotPython",
        u"DataRobot Python API Documentation",
        "DataRobot, Inc.",
        "DataRobotPython",
        "One line description of project.",
        "Miscellaneous",
    ),
]

# Documents to append as an appendix to all manuals.
# texinfo_appendices = []

# If false, no module index is generated.
# texinfo_domain_indices = True

# How to display URL addresses: 'footnote', 'no', or 'inline'.
# texinfo_show_urls = 'footnote'


def setup(app):
    app.add_css_file("css/tight_table.css")




###########################################################################
#          auto-created readthedocs.org specific configuration            #
###########################################################################


#
# The following code was added during an automated build on readthedocs.org
# It is auto created and injected for every build. The result is based on the
# conf.py.tmpl file found in the readthedocs.org codebase:
# https://github.com/rtfd/readthedocs.org/blob/main/readthedocs/doc_builder/templates/doc_builder/conf.py.tmpl
#
# Note: this file shouldn't rely on extra dependencies.

import importlib
import sys
import os.path

# Borrowed from six.
PY3 = sys.version_info[0] == 3
string_types = str if PY3 else basestring

from sphinx import version_info

# Get suffix for proper linking to GitHub
# This is deprecated in Sphinx 1.3+,
# as each page can have its own suffix
if globals().get('source_suffix', False):
    if isinstance(source_suffix, string_types):
        SUFFIX = source_suffix
    elif isinstance(source_suffix, (list, tuple)):
        # Sphinx >= 1.3 supports list/tuple to define multiple suffixes
        SUFFIX = source_suffix[0]
    elif isinstance(source_suffix, dict):
        # Sphinx >= 1.8 supports a mapping dictionary for multiple suffixes
        SUFFIX = list(source_suffix.keys())[0]  # make a ``list()`` for py2/py3 compatibility
    else:
        # default to .rst
        SUFFIX = '.rst'
else:
    SUFFIX = '.rst'

# Add RTD Static Path. Add to the end because it overwrites previous files.
if not 'html_static_path' in globals():
    html_static_path = []
if os.path.exists('_static'):
    html_static_path.append('_static')

# Add RTD Theme only if they aren't overriding it already
using_rtd_theme = (
    (
        'html_theme' in globals() and
        html_theme in ['default'] and
        # Allow people to bail with a hack of having an html_style
        'html_style' not in globals()
    ) or 'html_theme' not in globals()
)
if using_rtd_theme:
    theme = importlib.import_module('sphinx_rtd_theme')
    html_theme = 'sphinx_rtd_theme'
    html_style = None
    html_theme_options = {}
    if 'html_theme_path' in globals():
        html_theme_path.append(theme.get_html_theme_path())
    else:
        html_theme_path = [theme.get_html_theme_path()]

if globals().get('websupport2_base_url', False):
    websupport2_base_url = 'https://readthedocs.com/websupport'
    websupport2_static_url = 'https://assets.readthedocs.com/'


#Add project information to the template context.
context = {
    'using_theme': using_rtd_theme,
    'html_theme': html_theme,
    'current_version': "latest-release",
    'version_slug': "latest-release",
    'MEDIA_URL': "https://media.readthedocs.com/media/",
    'STATIC_URL': "https://assets.readthedocs.com/",
    'PRODUCTION_DOMAIN': "readthedocs.com",
    'proxied_static_path': "/_/static/",
    'versions': [
    ("v2.28.1", "/en/v2.28.1/"),
    ("v2.28.0", "/en/v2.28.0/"),
    ("v2.27.3", "/en/v2.27.3/"),
    ("v2.27.2", "/en/v2.27.2/"),
    ("v2.27.1", "/en/v2.27.1/"),
    ("v2.27.0", "/en/v2.27.0/"),
    ("v2.26.2", "/en/v2.26.2/"),
    ("v2.26.1", "/en/v2.26.1/"),
    ("v2.26.0", "/en/v2.26.0/"),
    ("v2.25.1", "/en/v2.25.1/"),
    ("v2.25.0", "/en/v2.25.0/"),
    ("v2.24.0", "/en/v2.24.0/"),
    ("v2.23.1", "/en/v2.23.1/"),
    ("v2.23.0", "/en/v2.23.0/"),
    ("v2.22.2", "/en/v2.22.2/"),
    ("v2.22.1", "/en/v2.22.1/"),
    ("v2.22.0", "/en/v2.22.0/"),
    ("v2.21.6", "/en/v2.21.6/"),
    ("v2.21.5", "/en/v2.21.5/"),
    ("v2.21.4", "/en/v2.21.4/"),
    ("v2.21.3", "/en/v2.21.3/"),
    ("v2.21.2", "/en/v2.21.2/"),
    ("v2.20.0", "/en/v2.20.0/"),
    ("v2.19.0", "/en/v2.19.0/"),
    ("v2.18.0", "/en/v2.18.0/"),
    ("v2.17.0", "/en/v2.17.0/"),
    ("v2.16.0", "/en/v2.16.0/"),
    ("v2.15.1", "/en/v2.15.1/"),
    ("v2.14.2", "/en/v2.14.2/"),
    ("v2.13.3", "/en/v2.13.3/"),
    ("v2.12.2", "/en/v2.12.2/"),
    ("v2.11.2", "/en/v2.11.2/"),
    ("v2.9.3", "/en/v2.9.3/"),
    ("v2.8.3", "/en/v2.8.3/"),
    ("v2.7.3", "/en/v2.7.3/"),
    ("early-access", "/en/early-access/"),
    ],
    'downloads': [ 
    ("pdf", "//datarobot-public-api-client.readthedocs-hosted.com/_/downloads/en/latest-release/pdf/"),
    ],
    'subprojects': [ 
    ],
    'slug': 'datarobot-public-api-client',
    'name': u'public_api_client',
    'rtd_language': u'en',
    'programming_language': u'py',
    'canonical_url': 'https://datarobot-public-api-client.readthedocs-hosted.com/en/v2.28.1/',
    'analytics_code': 'None',
    'single_version': False,
    'conf_py_path': '/docs/',
    'api_host': 'https://readthedocs.com',
    'github_user': 'datarobot',
    'proxied_api_host': '/_',
    'github_repo': 'public_api_client',
    'github_version': 'latest-release',
    'display_github': True,
    'bitbucket_user': 'None',
    'bitbucket_repo': 'None',
    'bitbucket_version': 'latest-release',
    'display_bitbucket': False,
    'gitlab_user': 'None',
    'gitlab_repo': 'None',
    'gitlab_version': 'latest-release',
    'display_gitlab': False,
    'READTHEDOCS': True,
    'using_theme': (html_theme == "default"),
    'new_theme': (html_theme == "sphinx_rtd_theme"),
    'source_suffix': SUFFIX,
    'ad_free': False,
    'docsearch_disabled': False,
    'user_analytics_code': '',
    'global_analytics_code': 'UA-17997319-2',
    'commit': '13f723fd',
}

# For sphinx >=1.8 we can use html_baseurl to set the canonical URL.
# https://www.sphinx-doc.org/en/master/usage/configuration.html#confval-html_baseurl
if version_info >= (1, 8):
    if not globals().get('html_baseurl'):
        html_baseurl = context['canonical_url']
    context['canonical_url'] = None





if 'html_context' in globals():
    
    html_context.update(context)
    
else:
    html_context = context

# Add custom RTD extension
if 'extensions' in globals():
    # Insert at the beginning because it can interfere
    # with other extensions.
    # See https://github.com/rtfd/readthedocs.org/pull/4054
    extensions.insert(0, "readthedocs_ext.readthedocs")
else:
    extensions = ["readthedocs_ext.readthedocs"]

# Add External version warning banner to the external version documentation
if 'tag' == 'external':
    extensions.insert(1, "readthedocs_ext.external_version_warning")
    readthedocs_vcs_url = 'None'
    readthedocs_build_url = 'https://readthedocs.com/projects/datarobot-public-api-client/builds/1119369/'

project_language = 'en'

# User's Sphinx configurations
language_user = globals().get('language', None)
latex_engine_user = globals().get('latex_engine', None)
latex_elements_user = globals().get('latex_elements', None)

# Remove this once xindy gets installed in Docker image and XINDYOPS
# env variable is supported
# https://github.com/rtfd/readthedocs-docker-images/pull/98
latex_use_xindy = False

chinese = any([
    language_user in ('zh_CN', 'zh_TW'),
    project_language in ('zh_CN', 'zh_TW'),
])

japanese = any([
    language_user == 'ja',
    project_language == 'ja',
])

if chinese:
    latex_engine = latex_engine_user or 'xelatex'

    latex_elements_rtd = {
        'preamble': '\\usepackage[UTF8]{ctex}\n',
    }
    latex_elements = latex_elements_user or latex_elements_rtd
elif japanese:
    latex_engine = latex_engine_user or 'platex'

# Make sure our build directory is always excluded
exclude_patterns = globals().get('exclude_patterns', [])
exclude_patterns.extend(['_build'])

[rtd-command-info] start-time: 2022-09-29T16:23:14.150728Z, end-time: 2022-09-29T16:24:02.418077Z, duration: 48, exit-code: 0
python -m sphinx -T -E -b html -d _build/doctrees -D language=en . _build/html
Running Sphinx v1.8.3
loading translations [en]... done
making output directory...
building [mo]: targets for 0 po files that are out of date
building [html]: targets for 48 source files that are out of date
updating environment: 48 added, 0 changed, 0 removed
reading sources... [  2%] CHANGES
reading sources... [  4%] autodoc/api_reference
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: potentially wrong underline length... 
Yields 
------- in 
Get an iterator for the requested datasets a user can view.
This lazily retrieves results. It does not get the next page from the server until the... in the docstring of iterate in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/dataset.py.
  warn(msg)
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: potentially wrong underline length... 
Yields 
------- in 
Get an iterator for the requested features of a dataset.
This lazily retrieves results. It does not get the next page from the server until the... in the docstring of iterate_all_features in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/dataset.py.
  warn(msg)
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: Unknown section Example in the docstring of update_predictions_by_forecast_date_settings in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/deployment.py.
  warn(msg)
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: potentially wrong underline length... 
Returns 
-------- in 
Get a sample of the actual values used to measure the association between a pair of features
... in the docstring of get in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/feature_association_matrix/feature_association_matrix_details.py.
  warn(msg)
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: potentially wrong underline length... 
Notes 
------ in 
Feature Effects provides partial dependence and predicted vs actual values for top-500
features ordered by feature impact score.... in the docstring of FeatureEffects in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/feature_effect.py.
  warn(msg)
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: potentially wrong underline length... 
Notes 
------ in 
Feature Fit provides partial dependence and predicted vs actual values for top-500
features ordered by feature importance score.... in the docstring of FeatureFit in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/feature_fit.py.
  warn(msg)
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: potentially wrong underline length... 
Returns 
-------- in 
Get the association statistics and metadata for a project's
informative features... in the docstring of get_associations in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/project.py.
  warn(msg)
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: potentially wrong underline length... 
Returns 
-------- in 
List featurelists and get feature association status for each
... in the docstring of get_association_featurelists in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/project.py.
  warn(msg)
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: potentially wrong underline length... 
Returns 
-------- in 
Get a sample of the actual values used to measure the association
between a pair of features... in the docstring of get_association_matrix_details in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/project.py.
  warn(msg)
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: potentially wrong underline length... 
Raises 
---------- in 
Retrieve the input types which can be used with User Blueprints.
... in the docstring of get_input_types in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/user_blueprints/models.py.
  warn(msg)
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: potentially wrong underline length... 
Returns 
---------- in 
Retrieve the input types which can be used with User Blueprints.
... in the docstring of get_input_types in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/user_blueprints/models.py.
  warn(msg)
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: potentially wrong underline length... 
Raises 
---------- in 
Add a list of user blueprints, by id, to a specified (by id) project's repository.
... in the docstring of add_to_project in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/user_blueprints/models.py.
  warn(msg)
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: potentially wrong underline length... 
Returns 
---------- in 
Add a list of user blueprints, by id, to a specified (by id) project's repository.
... in the docstring of add_to_project in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/user_blueprints/models.py.
  warn(msg)
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: potentially wrong underline length... 
Raises 
---------- in 
Retrieve the available tasks, organized into categories, which can be used to create or
modify User Blueprints.... in the docstring of get_available_tasks in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/user_blueprints/models.py.
  warn(msg)
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: potentially wrong underline length... 
Returns 
---------- in 
Retrieve the available tasks, organized into categories, which can be used to create or
modify User Blueprints.... in the docstring of get_available_tasks in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/user_blueprints/models.py.
  warn(msg)
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: potentially wrong underline length... 
Raises 
---------- in 
Validate that each value assigned to specified task parameters are valid.
... in the docstring of validate_task_parameters in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/user_blueprints/models.py.
  warn(msg)
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: potentially wrong underline length... 
Returns 
---------- in 
Validate that each value assigned to specified task parameters are valid.
... in the docstring of validate_task_parameters in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/user_blueprints/models.py.
  warn(msg)
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: potentially wrong underline length... 
Raises 
---------- in 
Get a list of users, groups and organizations that have an access to this user blueprint
... in the docstring of list_shared_roles in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/user_blueprints/models.py.
  warn(msg)
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: potentially wrong underline length... 
Returns 
---------- in 
Get a list of users, groups and organizations that have an access to this user blueprint
... in the docstring of list_shared_roles in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/user_blueprints/models.py.
  warn(msg)
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: potentially wrong underline length... 
Raises 
---------- in 
Validate a user blueprint and return information about the inputs expected and outputs
provided by each task.... in the docstring of validate_blueprint in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/user_blueprints/models.py.
  warn(msg)
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: potentially wrong underline length... 
Raises 
---------- in 
Share a user blueprint with a user, group, or organization
... in the docstring of update_shared_roles in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/user_blueprints/models.py.
  warn(msg)
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: potentially wrong underline length... 
Returns 
---------- in 
Share a user blueprint with a user, group, or organization
... in the docstring of update_shared_roles in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/user_blueprints/models.py.
  warn(msg)
/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/latest-release/lib/python3.7/site-packages/numpydoc/docscrape.py:440: UserWarning: potentially wrong underline length... 
Notes 
---------- in 
Word cloud data for the model.
... in the docstring of WordCloud in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/latest-release/datarobot/models/word_cloud.py.
  warn(msg)
You have imported from the _experimental directory.
This directory is used for unreleased datarobot features.
Unless you specifically know better, you don't have the access to use this functionality in the app, so this code will not work.
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build succeeded.

The HTML pages are in _build/html.
Updating searchtools for Read the Docs search...

[rtd-command-info] start-time: 2022-09-29T16:24:02.659694Z, end-time: 2022-09-29T16:24:13.597220Z, duration: 10, exit-code: 0
python -m sphinx -b latex -D language=en -d _build/doctrees . _build/latex
Running Sphinx v1.8.3
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done
build succeeded.

The LaTeX files are in _build/latex.
Run 'make' in that directory to run these through (pdf)latex
(use `make latexpdf' here to do that automatically).
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[rtd-command-info] start-time: 2022-09-29T16:24:14.794058Z, end-time: 2022-09-29T16:24:14.860107Z, duration: 0, exit-code: 0
cat latexmkrc
$latex = 'latex ' . $ENV{'LATEXOPTS'} . ' %O %S';
$pdflatex = 'pdflatex ' . $ENV{'LATEXOPTS'} . ' %O %S';
$lualatex = 'lualatex ' . $ENV{'LATEXOPTS'} . ' %O %S';
$xelatex = 'xelatex --no-pdf ' . $ENV{'LATEXOPTS'} . ' %O %S';
$makeindex = 'makeindex -s python.ist %O -o %D %S';
add_cus_dep( "glo", "gls", 0, "makeglo" );
sub makeglo {
 return system( "makeindex -s gglo.ist -o '$_[0].gls' '$_[0].glo'" );
}

[rtd-command-info] start-time: 2022-09-29T16:24:15.066270Z, end-time: 2022-09-29T16:24:31.667701Z, duration: 16, exit-code: 0
latexmk -r latexmkrc -pdf -f -dvi- -ps- -jobname=datarobot-public-api-client -interaction=nonstopmode


... (truncated) ...

Command output too long. Truncated to last 1MB.

ment_id\T1/ptm/m/n/10 , \
T1/ptm/m/it/10 en-vi-ron-

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[]\T1/ptm/m/it/10 testing_status=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cus-tom_m
odel_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cus-

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[]\T1/ptm/m/n/10 target type of the cus-tom in-fer-ence model.

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\T1/ptm/m/it/10 datarobot.TARGET_TYPE.MULTICLASS\T1/ptm/m/n/10 , \T1/ptm/m/it/1
0 datarobot.TARGET_TYPE.UNSTRUCTURED\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/n/10 Target fea-ture name; it is op-tional(ignored if pro-vided)

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[]\T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get_type\T1/ptm/m/n/
10 , \T1/ptm/m/it/10 tar-get_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 lan-guag
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l=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 neg-a-tive_class_label=None\T1/ptm/m/n/1
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ss_labels=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 class_labels_file=None\T1/ptm/m/
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tm/m/n/10 , \T1/ptm/m/it/10 de-scrip-tion=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 
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e_class_label=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_threshold=None\
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[]\T1/ptm/m/n/10 the type of the im-age, ei-ther CUS-TOM_MODEL_IMAGE_TYPE.CUSTO
M_MODEL_IMAGE

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[]\T1/ptm/m/it/10 custom_model_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cus-tom_model
_version_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id=None\T1/ptm/m/n/10 , \T1
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\T1/ptm/m/n/10 “-file1.txt”), (“/home/user/Documents/myModel/folder/file2
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ta=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-
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[]\T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/ptm/m/it/10 de-scrip-tion=None\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 pro-gram-ming_language=None\T1/ptm/m/n/10 , \T1/ptm/
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er_context_path\T1/ptm/m/n/10 , \T1/ptm/m/it/10 la-bel=None\T1/ptm/m/n/10 , \T1
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\T1/ptm/m/n/10 See datarobot.enums.EXECUTION_ENVIRONMENT_VERSION_BUILD_STATUS
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\T1/ptm/m/n/10 tween 0 and 1 by DR. only ap-plies to cus-tom es-ti-ma-tors with
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10 , \T1/ptm/m/it/10 lan-guage=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 de-scrip-ti
on=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cal-i-

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t value

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[]\T1/ptm/m/it/10 id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-ator=None\T1/ptm/
m/n/10 , \T1/ptm/m/it/10 base_names=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 class_
name=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/n/10 [str] the full JDBC url, for ex-am-ple \T1/ptm/m/it/10 jdbc:po
stgresql://my.dbaddress.org:5432/my_db\T1/ptm/m/n/10 . 

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[]\T1/ptm/m/n/10 [str] op-tional, the full JDBC url, for ex-am-ple
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ptm/m/n/10 , \T1/ptm/m/it/10 up-dated=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 para
ms=None\T1/ptm/m/n/10 ,
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/m/n/10 , \T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cat-e-gories\T1/
ptm/m/n/10 , \T1/ptm/m/it/10 cre-ated_at\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-at
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1/ptm/m/n/10 , \T1/ptm/m/it/10 pass-word=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 c
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/ptm/m/n/10 , \T1/ptm/m/it/10 uri\T1/ptm/m/n/10 , \T1/ptm/m/it/10 data_persiste
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[]\T1/ptm/m/n/10 the uri to data-source like: - ‘-file_name.csv’ - ‘[][]$
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\T1/ptm/m/it/10 project=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 data=None\T1/ptm/m
/n/10 , \T1/ptm/m/it/10 mono-

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\T1/ptm/m/it/10 use_project_settings=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sup-
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[]|\T1/ptm/m/n/10 Graphical rep-re-sen-ta-tion

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\T1/ptm/m/n/10 | Data >> Show Ad-

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[]

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[]\T1/ptm/m/n/10 Upload a pre-dic-tion dataset that con-tains the tar-get col-u
mn \T1/pcr/m/n/10 PredictionDataset.
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[]\T1/ptm/m/n/10 If you just want to get gen-er-ated pre-dic-tions from a Pre-d
ic-tJob, you can use the \T1/pcr/m/n/10 PredictJob.
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[]\T1/ptm/m/n/10 The method for cre-at-ing job def-i-ni-tions is iden-ti-cal to
 the ex-ist-ing [][]\T1/pcr/m/sl/10 datarobot.models.
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[]\T1/ptm/m/n/10 Alternatively, you can use a short-hand ver-sion with [][]\T1/
pcr/m/sl/10 datarobot.models.
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[]\T1/ptm/m/n/10 Before ini-ti-at-ing the model re-place-ment re-quest, it is u
su-ally a good idea to use the
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[]\T1/ptm/m/n/10 Refer to [][]\T1/pcr/m/sl/10 ExecutionEnvironment[][] \T1/ptm/
m/n/10 for prop-er-ties of the ex-e-cu-tion en-vi-ron-ment ob-ject and
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[]\T1/ptm/m/it/10 weights=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-sponse_cap=No
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\T1/ptm/m/it/10 out_modeling_mode=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 events_c
ount=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pare_model_for_deployment=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 
con-

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\T1/ptm/m/it/10 ture_discovery_supervised_feature_reduction=True\T1/ptm/m/n/10 
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[]\T1/ptm/m/n/10 [string, op-tional] (New in ver-sion v2.8) Spec-i-

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\T1/ptm/m/n/10 fies the be-hav-ior of Sca-le-out mod-els for the project. This 
is one of

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\T1/pcr/m/n/10 datarobot.enums.SCALEOUT_MODELING_MODE\T1/ptm/m/n/10 . If \T1/pc
r/m/n/10 datarobot.enums.
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\T1/ptm/m/n/10 cu-lat-ing fair-ness. Can be one of \T1/pcr/m/n/10 proportionalP
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\T1/pcr/m/n/10 predictionBalance\T1/ptm/m/n/10 , \T1/pcr/m/n/10 trueFavorableAn
dUnfavorableRateParity \T1/ptm/m/n/10 or
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[]\T1/ptm/m/n/10 The sta-tus of the in-sight. One of \T1/pcr/m/n/10 datarobot.e
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id\T1/ptm/m/
n/10 , \T1/ptm/m/it/10 back-test=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 source=No
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[]\T1/ptm/m/n/10 Filter by \T1/ptm/m/it/10 sta-tus \T1/ptm/m/n/10 == Anoma-lyAs
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[]\T1/ptm/m/n/10 [string] The URL for the pre-dic-tion dataset (e.g.:

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\T1/ptm/m/n/10 A [][]\T1/ptm/m/it/10 com-pli-ance doc-u-men-ta-tion tem-plate[]
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[]\T1/ptm/m/n/10 target type of the cus-tom in-fer-ence model.

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[]\T1/ptm/m/n/10 Determines
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\T1/ptm/m/it/10 pre-dic-tion_threshold=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cla
ss_labels=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 class_labels_file=None\T1/ptm/m/
n/10 ,

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[]\T1/ptm/m/n/10 target type of the cus-

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\T1/ptm/m/n/10 tom in-fer-ence model. Val-ues: [\T1/ptm/m/it/10 datarobot.TARGE
T_TYPE.BINARY\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 datarobot.TARGET_TYPE.REGRESSION\T1/ptm/m/n/10 , \T1/ptm/m/it/1
0 datarobot.TARGET_TYPE.MULTICLASS\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 lan-guage=None\T1/p
tm/m/n/10 , \T1/ptm/m/it/10 de-scrip-tion=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 
tar-get_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pos-i-

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\T1/ptm/m/it/10 tive_class_label=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 neg-a-tiv
e_class_label=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_threshold=None\
T1/ptm/m/n/10 ,
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[]\T1/ptm/m/n/10 the type of the im-age, ei-ther CUS-TOM_MODEL_IMAGE_TYPE.CUSTO
M_MODEL_IMAGE

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\T1/ptm/m/n/10 if the test-ing at-tempt is us-ing a Cus-tom-Mod-e-lIm-age as it
s model or CUS-

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[]\T1/ptm/m/n/10 Determines

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[]\T1/ptm/m/it/10 custom_model_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cus-tom_model
_version_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id=None\T1/ptm/m/n/10 , \T1
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[]
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[]\T1/ptm/m/n/10 Determines
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\T1/ptm/m/n/10 “-file1.txt”), (“/home/user/Documents/myModel/folder/file2
.txt”,

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\T1/ptm/m/n/10 “-folder/file2.txt”)] or [“/home/user/Documents/myModel/fi
le1.txt”,

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[]\T1/ptm/m/n/10 Additional pa-ram-e-ters re-

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[]\T1/ptm/m/it/10 custom_model_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 base_environm
ent_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_major_update=True\T1/ptm/m/n/10 , \T1/ptm/m/it/10 folder_pat
h=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 files=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 files_to_delete=None
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 net-

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\T1/ptm/m/it/10 repli-cas=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-quired_metada
ta=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-
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\T1/ptm/m/n/10 “-file1.txt”), (“/home/user/Documents/myModel/folder/file2
.txt”,

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\T1/ptm/m/n/10 “-folder/file2.txt”)] or [“/home/user/Documents/myModel/fi
le1.txt”,

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[]\T1/ptm/m/n/10 Additional pa-ram-e-ters re-
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[]\T1/ptm/m/n/10 Additional pa-ram-e-ters re-
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[]\T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/ptm/m/it/10 de-scrip-tion=None\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 pro-gram-ming_language=None\T1/ptm/m/n/10 , \T1/ptm/
m/it/10 re-
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[]\T1/ptm/m/it/10 execution_environment_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dock
er_context_path\T1/ptm/m/n/10 , \T1/ptm/m/it/10 la-bel=None\T1/ptm/m/n/10 , \T1
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\T1/ptm/m/n/10 See datarobot.enums.EXECUTION_ENVIRONMENT_VERSION_BUILD_STATUS
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\T1/ptm/m/n/10 tween 0 and 1 by DR. only ap-plies to cus-tom es-ti-ma-tors with
 tar-get type
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[]\T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get_type\T1/ptm/m/n/
10 , \T1/ptm/m/it/10 lan-guage=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 de-scrip-ti
on=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cal-i-

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[]\T1/ptm/m/n/10 the tar-get typed
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[]\T1/ptm/m/n/10 whether anomaly pre-dic-tions should be

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\T1/ptm/m/n/10 cal-i-brated to be be-tween 0 and 1 by DR. if None, uses de-faul
t value

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\T1/ptm/m/n/10 from DR app (True). only ap-plies to cus-tom es-ti-ma-tors with 
tar-get type
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[]\T1/ptm/m/it/10 id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 file_name\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 file_source\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-
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\T1/ptm/m/it/10 is_major_update=True\T1/ptm/m/n/10 , \T1/ptm/m/it/10 folder_pat
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[]\T1/ptm/m/it/10 custom_task_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 base_environme
nt_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 files_to_delete=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-quired_
metadata=None\T1/ptm/m/n/10 ,

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max-i-
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[]\T1/ptm/m/n/10 Additional pa-ram-e-ters re-
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[]\T1/ptm/m/n/10 Additional pa-ram-e-ters re-
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[]\T1/ptm/m/it/10 id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-ator=None\T1/ptm/
m/n/10 , \T1/ptm/m/it/10 base_names=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 class_
name=None\T1/ptm/m/n/10 ,
[210] [211] [212] [213] [214]
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[]\T1/ptm/m/n/10 [str] the full JDBC url, for ex-am-ple \T1/ptm/m/it/10 jdbc:po
stgresql://my.dbaddress.org:5432/my_db\T1/ptm/m/n/10 . 

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[]\T1/ptm/m/n/10 [str] op-tional, the full JDBC url, for ex-am-ple
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[]\T1/ptm/m/it/10 data_source_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 data_sour
ce_type=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 canon-i-

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\T1/ptm/m/it/10 cal_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-ator=None\T1/
ptm/m/n/10 , \T1/ptm/m/it/10 up-dated=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 para
ms=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 username=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pass-word=None\
T1/ptm/m/n/10 , \T1/ptm/m/it/10 do_snapshot=None\T1/ptm/m/n/10 , \T1/ptm/m/it/1
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\T1/ptm/m/it/10 sist_data_after_ingestion=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 
cat-e-gories=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-den-tial_id=None\T1/ptm/m
/n/10 ,
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[]\T1/ptm/m/it/10 dataset_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ver-sion_id\T1/ptm
/m/n/10 , \T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cat-e-gories\T1/
ptm/m/n/10 , \T1/ptm/m/it/10 cre-ated_at\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-at
ed_by\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 file_path=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 file-like=None
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cat-e-gories=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 data_frame=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 records=None\
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\T1/ptm/m/it/10 e-gories=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 read_timeout=600\
T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 data_source_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 user-name=None
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tm/m/n/10 , \T1/ptm/m/it/10 per-
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[]\T1/ptm/m/it/10 generator_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id=None\
T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 offset=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 limit=None\T1/ptm
/m/n/10 , \T1/ptm/m/it/10 cat-e-gory=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 or-de
r_by=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fil-
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[]\T1/ptm/m/it/10 project_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 user=None\T
1/ptm/m/n/10 , \T1/ptm/m/it/10 pass-word=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 c
re-den-tial_id=None\T1/ptm/m/n/10 ,

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ptm/m/n/10 , \T1/ptm/m/it/10 file-like=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 cat-e-gories=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 read_timeout=
600\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 dataset_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 data_frame=None\T1
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[]\T1/ptm/m/it/10 dataset_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 data_source_id\T1/
ptm/m/n/10 , \T1/ptm/m/it/10 user-

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e\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 dataset_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ver-sion_id\T1/ptm
/m/n/10 , \T1/ptm/m/it/10 cat-e-gories\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-ated
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\T1/ptm/m/it/10 ated_at\T1/ptm/m/n/10 , \T1/ptm/m/it/10 data_source_type\T1/ptm
/m/n/10 , \T1/ptm/m/it/10 er-ror\T1/ptm/m/n/10 , \T1/ptm/m/it/10 is_latest_vers
ion\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 last_modifier_full_name\T1/ptm/m/n/10 , \T1/ptm/m/it/10 name\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 uri\T1/ptm/m/n/10 , \T1/ptm/m/it/10 data_persiste
d=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 data_engine_query_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 data_
source_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 de-scrip-tion=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 eda1_modific
ation_date=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 eda1_modifier_full_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fe
a-ture_count=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 fea-ture_count_by_type=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pro
-cess-ing_state=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/n/10 the uri to data-source like: - ‘-file_name.csv’ - ‘[][]$
jdbc : DATA _
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 fore-

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\T1/ptm/m/it/10 tions\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 back-

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\T1/ptm/m/it/10 test_metadata\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 hold-

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[]\T1/ptm/m/n/10 Status back-test/holdout train-ing. One of \T1/pcr/m/n/10 data
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[]\T1/ptm/m/n/10 Status back-test/holdout val-i-da-tion. One of \T1/pcr/m/n/10 
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[]\T1/ptm/m/n/10 A list of \T1/pcr/m/n/10 datarobot.enums.

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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 start_date\T1/ptm/m/n/10 ,

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rt_time=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/n/10 You might find it more con-ve-nient to use [][]\T1/pcr/m/sl/10
 Feature.get_pairwise_conditional_probabilities[][]
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[]\T1/ptm/m/it/10 data\T1/ptm/m/n/10 , \T1/ptm/m/it/10 com-pleted_resource_url=
None\T1/ptm/m/n/10 ,
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m/m/n/10 , \T1/ptm/m/it/10 fea-turelist_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/
10 fea-

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\T1/ptm/m/it/10 turelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id=Non
e\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sam-ple_pct=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-
ing_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-ing_end_date=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_type=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_category=
None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 is_frozen=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_n_clusters_dynamically_determined=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 blueprint_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-rics=None
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 data=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_increasing
_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 n_clusters=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 has_empty_clust
ers=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sup-

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\T1/ptm/m/it/10 ports_monotonic_constraints=None\T1/ptm/m/n/10 , \T1/ptm/m/it/1
0 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tion_threshold=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tio
n_threshold_read_only=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_number=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 par-ent_model
_id=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 sample_pct=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_
id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 scor-ing_type=None\T1/ptm/m/n/10 , \T1/
ptm/m/it/10 train-

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\T1/ptm/m/it/10 ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_i
ncreasing_featurelist_id=<object ob-ject>\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-


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[]\T1/ptm/m/n/10 [str, op-tional] Ei-ther \T1/pcr/m/n/10 SCORING_TYPE.validatio
n \T1/ptm/m/n/10 or

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\T1/pcr/m/n/10 SCORING_TYPE.cross_validation\T1/ptm/m/n/10 . \T1/pcr/m/n/10 SCO
RING_TYPE.validation \T1/ptm/m/n/10 is
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[]\T1/ptm/m/it/10 featurelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing
_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_duration=None\T1/ptm/
m/n/10 ,

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\T1/ptm/m/it/10 time_window_sample_pct=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mon
o-tonic_increasing_featurelist_id=<object

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\T1/ptm/m/it/10 ob-ject>\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_decreasing_
featurelist_id=<object ob-ject>\T1/ptm/m/n/10 ,

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\T1/ptm/m/n/10 used to train the model. If spec-i-fied, nei-ther \T1/pcr/m/n/10
 training_duration \T1/ptm/m/n/10 nor
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n_intervals=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tion_intervals_size=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-c
ast_point=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tions_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-t
ions_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ac-

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\T1/ptm/m/it/10 tual_value_column=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-n
a-tion_algorithm=None\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 top_n_features
=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-
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/ptm/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 tures=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 row_count=None\T1/pt
m/m/n/10 ,
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[]\T1/ptm/m/it/10 training_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-ing_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-ing_end_date=None\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 data_subset\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-na-tion_al
gorithm=None\T1/ptm/m/n/10 ,
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\T1/ptm/m/it/10 turelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id=Non
e\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sam-ple_pct=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-
ing_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-ing_end_date=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_type=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_category=
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\T1/ptm/m/it/10 score=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_increasin
g_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mono-tonic_decreasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 sup-ports_monotonic_constraints=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_th
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\T1/ptm/m/it/10 tion_threshold_read_only=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 m
odel_number=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 source\T1/ptm/m/n/10 , \T1/ptm/m/it/10 top_n_features=None\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 tures=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 row_count=None\T1/pt
m/m/n/10 ,
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
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[]\T1/ptm/m/it/10 row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 top_n_features
=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-
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[]\T1/ptm/m/it/10 dataset_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 in-clude_predictio
n_intervals=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tion_intervals_size=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-c
ast_point=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tions_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-t
ions_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ac-

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\T1/ptm/m/it/10 tual_value_column=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-n
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[]\T1/ptm/m/it/10 data_subset\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-na-tion_al
gorithm=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-
ing_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
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\T1/ptm/m/it/10 model_type=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_category=
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\T1/ptm/m/it/10 is_frozen=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 blueprint_id=Non
e\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-

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\T1/ptm/m/it/10 mono-tonic_increasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mono-tonic_decreasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 sup-ports_monotonic_constraints=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_th
reshold=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pre-dic-tion_threshold_read_only=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_number=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 par-ent_model
_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sup-
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[]\T1/ptm/m/it/10 source\T1/ptm/m/n/10 , \T1/ptm/m/it/10 top_n_features=None\T1
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\T1/ptm/m/it/10 tures=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 row_count=None\T1/pt
m/m/n/10 ,
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
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[]\T1/ptm/m/it/10 row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 top_n_features
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[]\T1/ptm/m/it/10 training_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
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[]\T1/ptm/m/it/10 dataset_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 in-clude_predictio
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\T1/ptm/m/it/10 tion_intervals_size=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-c
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\T1/ptm/m/it/10 tions_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-t
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\T1/ptm/m/it/10 tual_value_column=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-n
a-tion_algorithm=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 data_subset\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-na-tion_al
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[]\T1/ptm/m/it/10 sample_pct=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_
id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 scor-ing_type=None\T1/ptm/m/n/10 , \T1/
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ncreasing_featurelist_id=<object ob-ject>\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-

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[]\T1/ptm/m/n/10 [str, op-tional] Ei-ther \T1/pcr/m/n/10 SCORING_TYPE.validatio
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\T1/pcr/m/n/10 SCORING_TYPE.cross_validation\T1/ptm/m/n/10 . \T1/pcr/m/n/10 SCO
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t=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-

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e=None\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
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[]\T1/ptm/m/it/10 training_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
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[]\T1/ptm/m/it/10 dataset_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 in-clude_predictio
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
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[]\T1/ptm/m/it/10 featurelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing
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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
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\T1/ptm/m/it/10 model_type=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_category=
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\T1/ptm/m/it/10 is_frozen=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 blueprint_id=Non
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odel_number=None\T1/ptm/m/n/10 ,
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\T1/ptm/m/it/10 fea-turelist_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_
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\T1/ptm/m/it/10 ing_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_type=No
ne\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_category=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 is_frozen=N
one\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 blueprint_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-rics=None
\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 tonic_increasing_featurelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/
it/10 mono-

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\T1/ptm/m/it/10 tonic_decreasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 sup-ports_monotonic_constraints=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_th
reshold=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pre-dic-tion_threshold_read_only=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_number=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sup-
[409] [410] [411] [412] [413] [414] [415] [416] [417] [418]
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[]\T1/ptm/m/it/10 source\T1/ptm/m/n/10 , \T1/ptm/m/it/10 top_n_features=None\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 tures=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 row_count=None\T1/pt
m/m/n/10 ,
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[]
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from

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[]\T1/ptm/m/it/10 row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 top_n_features
=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-
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[]\T1/ptm/m/it/10 training_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-ing_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-ing_end_date=None\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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[]\T1/ptm/m/it/10 dataset_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 in-clude_predictio
n_intervals=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tion_intervals_size=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-c
ast_point=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tions_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-t
ions_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ac-

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\T1/ptm/m/it/10 tual_value_column=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-n
a-tion_algorithm=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from

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[]\T1/ptm/m/it/10 data_subset\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-na-tion_al
gorithm=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 sample_pct=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_
id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 scor-ing_type=None\T1/ptm/m/n/10 , \T1/
ptm/m/it/10 train-

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\T1/ptm/m/it/10 ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_i
ncreasing_featurelist_id=<object ob-ject>\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-

[431]
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[]\T1/ptm/m/n/10 [str, op-tional] Ei-ther \T1/pcr/m/n/10 SCORING_TYPE.validatio
n \T1/ptm/m/n/10 or

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\T1/pcr/m/n/10 SCORING_TYPE.cross_validation\T1/ptm/m/n/10 . \T1/pcr/m/n/10 SCO
RING_TYPE.validation \T1/ptm/m/n/10 is

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[]\T1/ptm/m/it/10 featurelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing
_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_duration=None\T1/ptm/
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\T1/ptm/m/it/10 time_window_sample_pct=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mon
o-tonic_increasing_featurelist_id=<object

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\T1/ptm/m/it/10 ob-ject>\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_decreasing_
featurelist_id=<object ob-ject>\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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\T1/ptm/m/n/10 used to train the model. If spec-i-fied, nei-ther \T1/pcr/m/n/10
 training_duration \T1/ptm/m/n/10 nor
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[]\T1/ptm/m/n/10 [dict] “-Pa-ram-e-ter-s” dic-tio-nary, same as spec-i-fied
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[]\T1/ptm/m/it/10 eureqa_solution_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 com-plex-i
ty\T1/ptm/m/n/10 , \T1/ptm/m/it/10 er-

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\T1/ptm/m/it/10 ror\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pres-sion\T1/ptm/m/n/10 
, \T1/ptm/m/it/10 ex-pres-sion_annotated\T1/ptm/m/n/10 ,
[438]
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[]\T1/ptm/m/it/10 user_partition_col\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_
level\T1/ptm/m/n/10 , \T1/ptm/m/it/10 val-i-da-tion_level\T1/ptm/m/n/10 , \T1/p
tm/m/it/10 hold-out_level\T1/ptm/m/n/10 ,
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\T1/ptm/m/it/10 lot_data_selection_method=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 val-i-da-tion_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 hold-out_start_date=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 hold-out_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 dis-able_holdout=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 gap_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 num-

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\T1/ptm/m/it/10 ber_of_backtests=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 back-tests=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 use_time_series=False\T1/ptm/m/n/10 , \T1/ptm/m/it/10 de-

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\T1/ptm/m/it/10 fault_to_known_in_advance=False\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 de-fault_to_do_not_derive=False\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 ture_derivation_window_start=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 fea-ture_settings=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-

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\T1/ptm/m/it/10 cast_window_start=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 fore-cast_window_end=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 win-dows_basis_unit=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 treat_as_exponential=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 dif-fer-enc-ing_method=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 use_cross_series_features=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 ag-gre-ga-tion_type=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 cross_series_group_by_columns=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 su-per-vised_mode=False\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_splits=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 al-

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\T1/ptm/m/it/10 low_partial_history_time_series_predictions=False\T1/pcr/m/n/10
 ) 
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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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[]\T1/ptm/m/n/10 Note that ei-ther (\T1/pcr/m/n/10 holdout_start_date\T1/ptm/m/
n/10 , \T1/pcr/m/n/10 holdout_duration\T1/ptm/m/n/10 ) or (\T1/pcr/m/n/10 holdo
ut_start_date\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/n/10 [str] one of \T1/pcr/m/n/10 datarobot.enums.

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\T1/pcr/m/n/10 DATETIME_AUTOPILOT_DATA_SELECTION_METHOD\T1/ptm/m/n/10 . Whether
 mod-els cre-
[441]
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[]\T1/ptm/m/n/10 [datetime.datetime or None] The start date of hold-out scor-in
g

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[]\T1/ptm/m/n/10 [str or None] The du-ra-tion of the hold-out scor-ing data. If

[442]
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[]\T1/ptm/m/n/10 [list of Pe-ri-od-ic-ity, op-tional] (New in ver-sion v2.9) a 
list of

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[]\T1/ptm/m/n/10 (New in ver-sion v2.24)

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[]\T1/ptm/m/it/10 index\T1/ptm/m/n/10 , \T1/ptm/m/it/10 gap_duration=None\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 val-i-da-

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\T1/ptm/m/it/10 tion_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 val-i-da-t
ion_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 val-i-da-tion_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pri
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\T1/ptm/m/it/10 mary_training_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 p
ri-
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\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string s
uch as those re-turned by the

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[]\T1/ptm/m/n/10 Option 2 (New in ver-sion v2.20): Use \T1/pcr/m/n/10 index\T1/
ptm/m/n/10 , \T1/pcr/m/n/10 primary_training_start_date\T1/ptm/m/n/10 ,

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\T1/pcr/m/n/10 primary_training_end_date\T1/ptm/m/n/10 , \T1/pcr/m/n/10 validat
ion_start_date\T1/ptm/m/n/10 , and \T1/pcr/m/n/10 validation_end_date\T1/ptm/m/
n/10 .

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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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[]\T1/ptm/m/it/10 feature_name\T1/ptm/m/n/10 , \T1/ptm/m/it/10 known_in_advance
=None\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 project_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 date-time_par
tition_column=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 date_format=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 au-topi-

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\T1/ptm/m/it/10 lot_data_selection_method=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 val-i-da-tion_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ava
il-

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\T1/ptm/m/it/10 able_training_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 a
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\T1/ptm/m/it/10 able_training_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ava
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\T1/ptm/m/it/10 able_training_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 av
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\T1/ptm/m/it/10 able_training_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pri
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\T1/ptm/m/it/10 mary_training_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 p
ri-

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\T1/ptm/m/it/10 mary_training_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pri
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\T1/ptm/m/it/10 mary_training_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pr
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\T1/ptm/m/it/10 gap_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 gap_row_count
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\T1/ptm/m/it/10 gap_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dis-able_hold
out=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 hold-

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\T1/ptm/m/it/10 out_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 hold-out_du
ration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 hold-out_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 hold-ou
t_end_date=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 num-ber_of_backtests=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 back-
tests=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 to-

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\T1/ptm/m/it/10 tal_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 use_time_ser
ies=False\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 de-fault_to_known_in_advance=False\T1/ptm/m/n/10 ,

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 fea-

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\T1/ptm/m/it/10 ture_settings=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_wi
ndow_start=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 dows_basis_unit=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 treat_as_e
xponential=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 dif-fer-enc-ing_method=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pe-
ri-od-ic-i-

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\T1/ptm/m/it/10 ties=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mul-ti-series_id_colu
mns=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 num-ber_of_do_not_derive_features=0\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 use_cross_series_features=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 ag-gre-ga-tion_type=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 cross_series_group_by_columns=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 dar_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_splits=None
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 al-
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[]\T1/ptm/m/n/10 [str] one of \T1/pcr/m/n/10 datarobot.enums.

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\T1/pcr/m/n/10 DATETIME_AUTOPILOT_DATA_SELECTION_METHOD\T1/ptm/m/n/10 . Whether
 mod-els cre-ated
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[]\T1/ptm/m/n/10 [list of Pe-ri-od-ic-ity, op-tional] (New in ver-sion v2.9) a 
list of
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[]\T1/ptm/m/n/10 (New in ver-sion v2.24)
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\T1/pcr/m/n/10 primary_training_end_date\T1/ptm/m/n/10 , \T1/pcr/m/n/10 validat
ion_start_date\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 val-i-da-tion_end_date=None\T1/ptm/m/n/10 ,
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\T1/ptm/m/n/10 and \T1/pcr/m/n/10 validation_duration\T1/ptm/m/n/10 ). If \T1/p
cr/m/n/10 True\T1/ptm/m/n/10 , will use a start/end date ap-

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\T1/ptm/m/n/10 proach for spec-i-fy-ing back-tests (\T1/pcr/m/n/10 primary_trai
ning_start_date\T1/ptm/m/n/10 ,

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\T1/pcr/m/n/10 primary_training_end_date\T1/ptm/m/n/10 , \T1/pcr/m/n/10 validat
ion_start_date\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 name\T1/ptm/m/n/10
 , \T1/ptm/m/it/10 true_positive_value=1\T1/ptm/m/n/10 , \T1/ptm/m/it/10 true_n
egative_value=1\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 id\T1/ptm/m/n/10 ,
 \T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/ptm/m/it/10 true_positive_value\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 true_negative_value\T1/ptm/m/n/10 ,
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\T1/ptm/b/n/10 Note:  \T1/ptm/m/n/10 Dep-re-cated in v2.3 in fa-vor of \T1/pcr/
m/n/10 Project.upload_dataset \T1/ptm/m/n/10 and \T1/pcr/m/n/10 Model.

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[]\T1/ptm/m/n/10 [str] id of cre-ated job, can be used as pa-ram-e-ter to

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\T1/pcr/m/n/10 PredictJob.get \T1/ptm/m/n/10 or \T1/pcr/m/n/10 PredictJob.get_p
redictions \T1/ptm/m/n/10 meth-ods or
[458] [459]
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 id\T1/ptm/m/n/10 ,
 \T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-ated\T1/ptm/m/n/10 , 
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\T1/ptm/m/it/10 num_columns\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_point=Non
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\T1/ptm/m/it/10 con-tains_target_values=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 se
c-
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\T1/ptm/m/it/10 tion_explanations_sample=None\T1/pcr/m/n/10 ) 
[462]
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[]\T1/ptm/m/it/10 id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 ,
 \T1/ptm/m/it/10 model_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id\T1/ptm/m/n
/10 ,

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\T1/ptm/m/it/10 max_explanations\T1/ptm/m/n/10 , \T1/ptm/m/it/10 num_columns\T1
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id\T1/ptm/m/
n/10 , \T1/ptm/m/it/10 dataset_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_explanati
ons=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 thresh-

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[]\T1/ptm/m/n/10 Compute a Pre-dic-tion-Ex-pla-na-tion-sIni-tial-iza-tion for t
he model via \T1/pcr/m/n/10 datarobot.

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[]\T1/ptm/m/n/10 Compute pre-dic-tions for the model and dataset via \T1/pcr/m/
n/10 datarobot.Model.
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[]\T1/ptm/m/it/10 limit=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 off-set=None\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 ex-
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[]\T1/ptm/m/n/10 [list] an ar-ray of dic-tio-nar-ies with a schema de-scribed a
s
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_expla
nations_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 limit=None\T1/ptm/m/n/10 , \T1/ptm/m
/it/10 off-set=0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_id\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 model_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 dataset_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 in-cludes_predi
ction_intervals=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 cast_point=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tions_s
tart_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-

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\T1/ptm/m/it/10 dic-tions_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ac-tual
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\T1/ptm/m/it/10 ex-pla-na-tion_algorithm=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 m
ax_explanations=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id=None\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 par-ent_model_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 rule-set_id=None\
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\T1/ptm/m/it/10 tar-get=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get_type=None\
T1/ptm/m/n/10 , \T1/ptm/m/it/10 hold-out_unlocked=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 met-ric=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 stage=None\T1/ptm/
m/n/10 , \T1/ptm/m/it/10 par-ti-tion=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pos-i
-

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\T1/ptm/m/it/10 tive_class=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-ated=None\T
1/ptm/m/n/10 , \T1/ptm/m/it/10 ad-vanced_options=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 rec-om-mender=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_train_pc
t=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 max_train_rows=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sca-le-out_
max_train_pct=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 sca-le-out_max_train_rows=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 
file_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-den-

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\T1/ptm/m/it/10 tials=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-ture_engineering
_prediction_point=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 tion-ships_configuration_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/1
0 project_description=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 sourcedata\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_name='Unti
tled Project'\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_wait=600\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 data_source_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 user-name\T1/p
tm/m/n/10 , \T1/ptm/m/it/10 pass-word\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 dataset_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_version_id
=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 project_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 user=None\T1/
ptm/m/n/10 , \T1/ptm/m/it/10 pass-word=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 cre-den-tial_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 use_kerber
os=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-den-
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[]\T1/ptm/m/it/10 sourcedata\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get=None\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 project_name='Untitled Project'\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 worker_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-ric=None\
T1/ptm/m/n/10 , \T1/ptm/m/it/10 au-topi-lot_on=True\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pos-i-tive_class=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get_t
ype=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 un-su-per-vised_mode=False\T1/ptm/m/n/
10 ,

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\T1/ptm/m/it/10 blend_best_models=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-pare
_model_for_deployment=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 con-

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\T1/ptm/m/it/10 sider_blenders_in_recommendation=None\T1/ptm/m/n/10 , \T1/ptm/m
/it/10 scor-ing_code_only=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 fea-ture_discovery_supervised_feature_reduction=None\T1/ptm/m/n
/10 , \T1/ptm/m/it/10 un-su-per-
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[]\T1/ptm/m/n/10 Compute “All back-test”

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[]\T1/ptm/m/n/10 (New
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[]\T1/ptm/m/n/10 Project.list(search_params={‘project_name’: ‘red’}) >>
>
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\T1/ptm/m/it/10 pos-i-tive_class=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 par-ti-ti
on-ing_method=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_id=None\T1/ptm/
m/n/10 , \T1/ptm/m/it/10 ad-

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\T1/ptm/m/it/10 vanced_options=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_wait=60
0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get_type=None\T1/ptm/m/n/10 , \T1/ptm/m/i
t/10 cre-den-tials=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 fea-ture_engineering_prediction_point=None\T1/ptm/m/n/10 , \T1/
ptm/m/it/10 un-su-per-vised_mode=False\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-la-ti
on-
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[]\T1/ptm/m/n/10 Raised if \T1/pcr/m/n/10 advanced_options\T1/ptm/m/n/10 , \T1/
pcr/m/n/10 partitioning_method \T1/ptm/m/n/10 or
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\T1/ptm/m/it/10 tions_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-t
ions_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_filename=None\T1/ptm
/m/n/10 ,

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\T1/ptm/m/it/10 re-lax_known_in_advance_features_check=None\T1/ptm/m/n/10 , \T1
/ptm/m/it/10 cre-den-tials=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ac-
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[]\T1/ptm/m/n/10 Raised if \T1/pcr/m/n/10 forecast_point \T1/ptm/m/n/10 or \T1/
pcr/m/n/10 predictions_start_date \T1/ptm/m/n/10 and

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[]\T1/ptm/m/it/10 data_source_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 user-name\T1/p
tm/m/n/10 , \T1/ptm/m/it/10 pass-word\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 max_wait=600\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_point=No
ne\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-

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\T1/ptm/m/it/10 lax_known_in_advance_features_check=None\T1/ptm/m/n/10 , \T1/pt
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\T1/ptm/m/it/10 den-tials=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tions_st
art_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tions_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ac-tual_val
ue_column=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 dataset_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_version_id
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\T1/ptm/m/it/10 max_wait=600\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_point=No
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\T1/ptm/m/it/10 lax_known_in_advance_features_check=None\T1/ptm/m/n/10 , \T1/pt
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\T1/ptm/m/it/10 den-tials=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tions_st
art_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tions_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ac-tual_val
ue_column=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sec-
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[]\T1/ptm/m/it/10 featurelist_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mode='auto'\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 blend_best_models=True\T1/ptm/m/n/10 , \T1/ptm/m/
it/10 scor-

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\T1/ptm/m/it/10 ing_code_only=False\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-pare_mo
del_for_deployment=True\T1/ptm/m/n/10 , \T1/ptm/m/it/10 con-

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\T1/ptm/m/it/10 sider_blenders_in_recommendation=False\T1/ptm/m/n/10 , \T1/ptm/
m/it/10 run_leakage_removed_feature_list=True\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 trainable\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sam-ple_pct=None\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it
/10 source_project_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 scor-

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\T1/ptm/m/it/10 ing_type=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_row_cou
nt=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_increasing_featurelist_id=<o
bject

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[]\T1/ptm/m/n/10 [str, op-tional] Ei-ther \T1/pcr/m/n/10 SCORING_TYPE.validatio
n \T1/ptm/m/n/10 or

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\T1/pcr/m/n/10 SCORING_TYPE.cross_validation\T1/ptm/m/n/10 . \T1/pcr/m/n/10 SCO
RING_TYPE.validation \T1/ptm/m/n/10 is
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[]\T1/ptm/m/it/10 blueprint_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_id=
None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_row_count=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 source_
project_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-

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\T1/ptm/m/it/10 tonic_increasing_featurelist_id=<object ob-ject>\T1/ptm/m/n/10 
, \T1/ptm/m/it/10 mono-

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\T1/ptm/m/it/10 tonic_decreasing_featurelist_id=<object ob-ject>\T1/ptm/m/n/10 
, \T1/ptm/m/it/10 use_project_settings=False\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
 such as those re-turned by the
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\T1/ptm/m/n/10 used to train the model. If spec-i-fied, nei-ther \T1/pcr/m/n/10
 training_duration \T1/ptm/m/n/10 nor

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[]\T1/ptm/m/n/10 [str] Cho-sen blend method, one from \T1/pcr/m/n/10 datarobot.
enums.
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[]\T1/ptm/m/n/10 [str] Cho-sen blend method, one from \T1/pcr/m/n/10 datarobot.
enums.

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[]\T1/ptm/m/n/10 [QUEUE_STATUS enum, op-tional] If called with
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[]\T1/ptm/m/n/10 [QUEUE_STATUS enum, op-tional] If called with

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[]\T1/ptm/m/n/10 [QUEUE_STATUS enum, op-tional] If called with
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[]\T1/ptm/m/n/10 This should be VER-BOSITY_LEVEL.SILENT or VER-
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[]\T1/ptm/m/n/10 If value of \T1/pcr/m/n/10 variable_type \T1/ptm/m/n/10 is not
 from \T1/pcr/m/n/10 datarobot.enums.
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[]\T1/ptm/m/it/10 catalog_version_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 date-
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[]\T1/ptm/m/it/10 supported\T1/ptm/m/n/10 , \T1/ptm/m/it/10 rea-
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id\T1/ptm/m/
n/10 , \T1/ptm/m/it/10 rea-
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[]\T1/ptm/m/n/10 Compute a Rea-son-CodesIni-tial-iza-tion for the model via \T1
/pcr/m/n/10 datarobot.

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[]\T1/ptm/m/n/10 Compute pre-dic-tions for the model and dataset via \T1/pcr/m/
n/10 datarobot.Model.
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[]\T1/ptm/m/it/10 row_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion\T1/ptm/m/
n/10 , \T1/ptm/m/it/10 pre-dic-

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[]\T1/ptm/m/n/10 [list] an ar-ray of dic-tio-nar-ies with a schema de-scribed a
s

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\T1/ptm/m/it/10 next=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 data=None\T1/ptm/m/n/
10 , \T1/ptm/m/it/10 rea-

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\T1/ptm/m/it/10 son_codes_record_location=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 rea-son_codes_id\T
1/ptm/m/n/10 , \T1/ptm/m/it/10 limit=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 off-s
et=0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-
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[]\T1/ptm/m/it/10 source\T1/ptm/m/n/10 , \T1/ptm/m/it/10 roc_points\T1/ptm/m/n/
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\T1/ptm/m/it/10 a-tive_class_predictions\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pos-i-tive_class_predictions\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 source_model_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 la-bel\T1/ptm/m
/n/10 , \T1/ptm/m/it/10 kol-
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[]\T1/ptm/m/it/10 project_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 par-ent_model
_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 sample_pct=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_
id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 scor-ing_type=None\T1/ptm/m/n/10 , \T1/
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\T1/ptm/m/it/10 ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_i
ncreasing_featurelist_id=<object ob-ject>\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-

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[]\T1/ptm/m/it/10 featurelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing
_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_duration=None\T1/ptm/
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\T1/ptm/m/it/10 time_window_sample_pct=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mon
o-tonic_increasing_featurelist_id=<object

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\T1/ptm/m/it/10 ob-ject>\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_decreasing_
featurelist_id=<object ob-ject>\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 time_window_sample_pct=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 f
ea-turelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_row_count=None\T1
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\T1/ptm/m/it/10 train-ing_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-i
ng_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_end_date=None\T1/p
tm/m/n/10 , \T1/ptm/m/it/10 sam-

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[]\T1/ptm/m/it/10 training_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-ing_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-ing_end_date=None\T1/ptm/m/n/10 ,
[515]
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get\T1/ptm/m/n
/10 , \T1/ptm/m/it/10 use_time_series=False\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dat
e-

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\T1/ptm/m/it/10 time_partition_column=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mul-
ti-series_id_columns=None\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 project_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 seg-ment=None
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_stage=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 project_status_error=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 au-to
pi-lot_done=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 client\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 path\T1/ptm/m/n/10 ,

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[518]
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[]\T1/ptm/m/n/10 [float] The to-tal of SHAP val-ues for fea-tures be-yond the
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\T1/ptm/m/it/10 pre-dic-tion_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 data_subset=None\T1/ptm/m/n/10 ,

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[520] [521] [522]
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[]\T1/ptm/m/it/10 blender\T1/ptm/m/n/10 , \T1/ptm/m/it/10 blueprint_id\T1/ptm/m
/n/10 , \T1/ptm/m/it/10 di-a-gram\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-tures\T1/
ptm/m/n/10 , \T1/ptm/m/it/10 fea-tures_text\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 icons\T1/ptm/m/n/10 , \T1/ptm/m/it/10 in-sights\T1/ptm/m/n/10 ,
 \T1/ptm/m/it/10 model_type\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sup-ported_target_t
ypes\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 user_blueprint_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 user_id\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 is_time_series=False\T1/ptm/m/n/10 , \T1/ptm/m/it/10
 ref-er-

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sion_metadata=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 hex_column_name_lookup=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pro
ject_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ver-
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[]\T1/ptm/m/it/10 custom_task_version_id\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 blueprint_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 model_type=None\T1/ptm/m/n/10 ,

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None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 output_method\T1/ptm/m/n/10 , \T1/ptm/m/it/10 task_code\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 task_parameters\T1/ptm/m/n/10 ,

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[]

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1/ptm/m/n/10 , \T1/ptm/m/it/10 off-set=0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 id=Non
e\T1/ptm/m/n/10 ,

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/n/10 , \T1/ptm/m/it/10 limit=100\T1/ptm/m/n/10 , \T1/ptm/m/it/10 off-set=0\T1/
ptm/m/n/10 ,

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\T1/ptm/m/it/10 owner_user_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 owner_userna
me=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 order_by='-
[529] [530] [531] [532]
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[]\T1/ptm/m/it/10 id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/p
tm/m/it/10 project_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 min_transformation_probability\T1/ptm/m/n/10 ,

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[533]
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[]\T1/ptm/m/it/10 id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/p
tm/m/it/10 project_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 trans-for-ma-tion_probability=0.0\T1/ptm/m/n/10 , \T1/ptm/m/it/
10 num-ber_of_new_images=1\T1/ptm/m/n/10 , \T1/ptm/m/it/10 trans-for-ma-

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n/10 ,

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\T1/ptm/m/it/10 height\T1/ptm/m/n/10 , \T1/ptm/m/it/10 width\T1/ptm/m/n/10 , \T
1/ptm/m/it/10 orig-i-

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\T1/ptm/m/it/10 nal_image_id=None\T1/ptm/m/n/10 ,
[534] [535] [536]
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[]\T1/ptm/m/n/10 Addded new pa-ram-e-ter \T1/ptm/m/it/10 up-load_read_timeout \
T1/ptm/m/n/10 to [][]\T1/pcr/m/sl/10 BatchPredictionJob.score[][] \T1/ptm/m/n/1
0 and

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[]\T1/ptm/m/n/10 Added the abil-ity to turn off su-per-vised fea-ture re-duc-ti
on for Time Se-ries projects. Op-tion
[537] [538]
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[]

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[539]
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[]\T1/ptm/m/n/10 Added the abil-ity to re-trieve de-tected er-rors in the po-te
n-tially mul-ti-cat-e-gor-i-cal fea-

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\T1/ptm/m/n/10 ture types that pre-vented the fea-ture to be iden-ti-fied as mu
l-ti-cat-e-gor-i-cal. [][]\T1/pcr/m/sl/10 Project.

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[][][]\T1/pcr/m/sl/10 datarobot.models.restore_discarded_features.DiscardedFeat
uresInfo.
[540]
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[][][]\T1/pcr/m/sl/10 datarobot.models.restore_discarded_features.DiscardedFeat
uresInfo.

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[]\T1/ptm/m/n/10 Added the abil-ity to con-trol class map-ping ag-gre-ga-tion i
n mul-ti-class projects via

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[]\T1/ptm/m/n/10 Added the abil-ity to com-pute and re-trieve Fea-ture Ef-fects
 for a Mul-ti-class model us-ing

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[][]\T1/pcr/m/sl/10 datarobot.models.Model.request_feature_effects_multiclass()
[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 datarobot.

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\T1/pcr/m/sl/10 models.Model.get_feature_effects_multiclass()[][] \T1/ptm/m/n/1
0 or [][]\T1/pcr/m/sl/10 datarobot.models.Model.

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[][]\T1/pcr/m/sl/10 datarobot.models.DatetimeModel.get_feature_effects_multicla
ss()[][] \T1/ptm/m/n/10 or

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[][]\T1/pcr/m/sl/10 datarobot.models.DatetimeModel.get_or_request_feature_effec
ts_multiclass()[][]

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[][][]\T1/pcr/m/sl/10 datarobot.models.Deployment.get_predictions_by_forecast_d
ate_settings()[][] 

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[][][]\T1/pcr/m/sl/10 datarobot.models.Deployment.update_predictions_by_forecas
t_date_settings()[][] 
[541]
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[]\T1/ptm/m/n/10 Added the abil-ity to re-quest a spe-cific model be pre-pared 
for de-ploy-ment us-ing [][]\T1/pcr/m/sl/10 Project.
[542] [543]
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[]\T1/ptm/m/n/10 Try to read Fea-ture Im-pact re-sult if ex-ist-ing jo-bId is N
one in [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 Updated [][]\T1/pcr/m/sl/10 project.start_autopilot[][] \T1/pt
m/m/n/10 to ac-cept the fol-low-ing new GA pa-ram-e-ters

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\T1/ptm/m/n/10 that are al-ready in the pub-lic API: \T1/pcr/m/n/10 consider_bl
enders_in_recommendation\T1/ptm/m/n/10 ,
[544] [545]
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[]\T1/ptm/m/n/10 Added new helper func-tion to projects to re-trieve the rec-om
-mended model. [][]\T1/pcr/m/sl/10 Project.

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[]\T1/ptm/m/n/10 Added \T1/pcr/m/n/10 docker_context_size \T1/ptm/m/n/10 and \T
1/pcr/m/n/10 docker_image_size \T1/ptm/m/n/10 to [][]\T1/pcr/m/sl/10 datarobot.

[546]
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[]\T1/ptm/m/n/10 Arguments \T1/pcr/m/n/10 dataset_definitions \T1/ptm/m/n/10 an
d \T1/pcr/m/n/10 relationships \T1/ptm/m/n/10 now ac-cept [][]\T1/pcr/m/sl/10 D
atasetDefinition[][]

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\T1/pcr/m/n/10 allow_partial_history_time_series_predictions \T1/ptm/m/n/10 at-
tribute of the [][]\T1/pcr/m/sl/10 datarobot.

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[]\T1/ptm/m/n/10 Add meth-ods to re-trieve per-class lift chart data for mul-ti
-l-abel mod-els: [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 Add meth-ods to re-trieve la-bel-wise ROC curves for mul-ti-l-
abel mod-els: [][]\T1/pcr/m/sl/10 Model.

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\T1/pcr/m/sl/10 get_pairwise_correlations[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/
10 Feature.get_pairwise_joint_probabilities[][] \T1/ptm/m/n/10 and

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[]

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[547]
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[]\T1/ptm/m/n/10 Support for down-load-ing fea-ture dis-cov-ery train-ing or pr
e-dic-tion dataset us-ing [][]\T1/pcr/m/sl/10 Project.

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[]\T1/ptm/m/n/10 Added [][]\T1/pcr/m/sl/10 datarobot.models.FeatureAssociationM
atrix[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 datarobot.

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\T1/pcr/m/sl/10 models.FeatureAssociationMatrixDetails[][] \T1/ptm/m/n/10 and [
][]\T1/pcr/m/sl/10 datarobot.models.

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[]\T1/ptm/m/n/10 Remove \T1/pcr/m/n/10 desired_memory \T1/ptm/m/n/10 param from
 the fol-low-ing meth-ods: [][]\T1/pcr/m/sl/10 CustomInferenceModel.

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\T1/pcr/m/sl/10 create[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 CustomModelVersi
on.create_clean[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 CustomModelVersion.crea
te_clean[][]\T1/ptm/m/n/10 ,
[548]
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[]\T1/ptm/m/n/10 All per-class lift chart data is now avail-able for mul-ti-cla
ss mod-els us-ing [][]\T1/pcr/m/sl/10 Model.

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\T1/ptm/m/n/10 class can now use \T1/pcr/m/n/10 datarobot.TARGET_TYPE.ANOMALY \
T1/ptm/m/n/10 for its \T1/pcr/m/n/10 target_type \T1/ptm/m/n/10 pa-ram-e-ter.

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[]

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[]\T1/ptm/m/n/10 Add sup-port for start-ing and re-triev-ing Fea-ture Im-pact i
n-for-ma-tion for [][]\T1/pcr/m/sl/10 datarobot.

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[]

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[]\T1/ptm/m/n/10 Actions ap-plied to in-put data dur-ing au-to-mated fea-ture d
is-cov-ery can now be re-trieved us-ing

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[]

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[549]
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[]\T1/ptm/m/n/10 New \T1/pcr/m/n/10 sampling_method \T1/ptm/m/n/10 param in [][
]\T1/pcr/m/sl/10 Model.train_datetime[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 P
roject.train_datetime[][]\T1/ptm/m/n/10 ,

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[]

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\T1/pcr/m/n/10 featurelist_id\T1/ptm/m/n/10 , \T1/pcr/m/n/10 credentials_ids\T1
/ptm/m/n/10 , \T1/pcr/m/n/10 project_version \T1/ptm/m/n/10 and \T1/pcr/m/n/10 
is_default \T1/ptm/m/n/10 in

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[][][]\T1/pcr/m/sl/10 SecondaryDatasetConfigurations.

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[]\T1/ptm/m/n/10 Class \T1/pcr/m/n/10 FeatureEngineeringGraph \T1/ptm/m/n/10 ha
s been re-moved. Use [][]\T1/pcr/m/sl/10 datarobot.models.

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[]\T1/ptm/m/n/10 for

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[]\T1/ptm/m/n/10 for [][]\T1/pcr/m/sl/10 datarobot.models.

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[]
[550]
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[]\T1/ptm/m/n/10 Added new ar-gu-ment \T1/pcr/m/n/10 base_environment_id \T1/pt
m/m/n/10 to meth-ods [][]\T1/pcr/m/sl/10 CustomModelVersion.

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[]\T1/ptm/m/n/10 New fields \T1/pcr/m/n/10 base_environment_id \T1/ptm/m/n/10 a
nd \T1/pcr/m/n/10 dependencies \T1/ptm/m/n/10 to class [][]\T1/pcr/m/sl/10 data
robot.

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[]\T1/ptm/m/n/10 Added a new method to trig-ger Fea-ture Im-pact cal-cu-la-tion
 for a Cus-tom In-fer-ence Im-age:

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[]\T1/ptm/m/n/10 Added new pa-ram-e-ter \T1/ptm/m/it/10 met-ric \T1/ptm/m/n/10 
to [][]\T1/pcr/m/sl/10 datarobot.models.TargetDrift[][]\T1/ptm/m/n/10 , [][]\T1
/pcr/m/sl/10 datarobot.models.
[551]
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[]\T1/ptm/m/n/10 The de-fault value for the \T1/pcr/m/n/10 mode \T1/ptm/m/n/10 
param in [][]\T1/pcr/m/sl/10 Project.set_target[][] \T1/ptm/m/n/10 has been cha
nged from
[552]
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[]\T1/ptm/m/n/10 Added new ar-gu-ments \T1/pcr/m/n/10 explanation_algorithm \T1
/ptm/m/n/10 and \T1/pcr/m/n/10 max_explanations \T1/ptm/m/n/10 to method

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[][]\T1/pcr/m/sl/10 Model.request_training_predictions[][]\T1/ptm/m/n/10 . New 
fields \T1/pcr/m/n/10 explanation_algorithm\T1/ptm/m/n/10 ,

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\T1/ptm/m/n/10 New fields \T1/pcr/m/n/10 prediction_explanations \T1/ptm/m/n/10
 and \T1/pcr/m/n/10 shap_metadata \T1/ptm/m/n/10 have been added to class

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[][]\T1/pcr/m/sl/10 TrainingPredictionsIterator[][] \T1/ptm/m/n/10 that is re-t
urned by method [][]\T1/pcr/m/sl/10 TrainingPredictions.

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\T1/pcr/m/sl/10 request_predictions[][]\T1/ptm/m/n/10 . New fields \T1/pcr/m/n/
10 explanation_algorithm\T1/ptm/m/n/10 , \T1/pcr/m/n/10 max_explanations

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\T1/ptm/m/n/10 and \T1/pcr/m/n/10 shap_warnings \T1/ptm/m/n/10 have been added 
to class [][]\T1/pcr/m/sl/10 Predictions[][]\T1/ptm/m/n/10 . Method [][]\T1/pcr
/m/sl/10 Predictions.

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[]\T1/ptm/m/n/10 Added \T1/pcr/m/n/10 model_splits \T1/ptm/m/n/10 to [][]\T1/pc
r/m/sl/10 DatetimePartitioningSpecification[][] \T1/ptm/m/n/10 and to

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[][][]\T1/pcr/m/sl/10 Project.upload_dataset[][] \T1/ptm/m/n/10 and [][]\T1/pcr
/m/sl/10 Model.request_predictions[][] \T1/ptm/m/n/10 now ac-cept
[553]
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[]\T1/ptm/m/n/10 New warn-ing is added to \T1/pcr/m/n/10 data_quality_warnings 
\T1/ptm/m/n/10 of [][]\T1/pcr/m/sl/10 datarobot.models.

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[]\T1/ptm/m/n/10 Scores and in-sights on ex-ter-nal test sets can be re-trieved
 us-ing [][]\T1/pcr/m/sl/10 ExternalScores[][]\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/n/10 Deployments now sup-port pre-dic-tions data col-lec-tion that 
en-ables pre-dic-tion re-

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\T1/ptm/m/n/10 quests and re-sults to be saved in Pre-dic-tions Data Stor-age. 
See [][]\T1/pcr/m/sl/10 Deployment.

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\T1/pcr/m/sl/10 get_predictions_data_collection_settings[][] \T1/ptm/m/n/10 and
 [][]\T1/pcr/m/sl/10 Deployment.

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[]\T1/ptm/m/n/10 Batch Pre-dic-tion jobs now sup-port scor-ing to Azure and Goo
gle Cloud Stor-age with meth-ods

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[]

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[554]
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[]\T1/ptm/m/n/10 Secondary dataset con-fig-u-ra-tion re-trieve and dele-tion is
 eas-ier now though new

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[][]\T1/pcr/m/sl/10 SecondaryDatasetConfigurations.delete[][] \T1/ptm/m/n/10 so
ft deletes a Sec-ondary dataset con-fig-u-ra-tion.

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[]

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[555] [556]
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[]\T1/ptm/m/n/10 A new work-flow is avail-able for mak-ing pre-dic-tions in tim
e se-ries projects. To that end,

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[]\T1/ptm/m/n/10 Per-class lift chart data is now avail-able for mul-ti-class m
od-els us-ing [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 A new boolean at-tribute \T1/pcr/m/n/10 unsupervised_mode \T1/
ptm/m/n/10 was added to [][]\T1/pcr/m/sl/10 datarobot.

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\T1/pcr/m/sl/10 DatetimePartitioningSpecification[][]\T1/ptm/m/n/10 . When it i
s set to True, date-time par-

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\T1/ptm/m/n/10 ti-tion-ing for un-su-per-vised time se-ries projects will be co
n-structed for now-cast-ing:
[557]
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[]\T1/ptm/m/n/10 Time se-ries model de-ploy-ments now sup-port pre-dic-tion in-
ter-vals. See

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[][]\T1/pcr/m/sl/10 Deployment.get_prediction_intervals_settings[][] \T1/ptm/m/
n/10 and [][]\T1/pcr/m/sl/10 Deployment.

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\T1/ptm/m/n/10 a new op-tional pa-ram-e-ter \T1/pcr/m/n/10 prediction_intervals
_size \T1/ptm/m/n/10 has been added to [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 It’s now pos-si-ble to get fea-ture as-so-ci-a-tion sta-tuse
s for fea-turelists us-ing [][]\T1/pcr/m/sl/10 Project.
[558] [559]
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[]\T1/ptm/m/n/10 Monotonic con-straints are now sup-ported for OTV projects. To
 that end, the pa-ram-e-ters

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[]\T1/ptm/m/n/10 The method [][]\T1/pcr/m/sl/10 Model.get_supported_capabilitie
s[][] \T1/ptm/m/n/10 now has an ex-tra field
[560] [561] [562]
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[]\T1/ptm/m/n/10 The dataframe re-turned from [][]\T1/pcr/m/sl/10 datarobot.Pre
dictionExplanations.

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[]\T1/ptm/m/n/10 Information re-trieval for [][]\T1/pcr/m/sl/10 ROC Curve[][] \
T1/ptm/m/n/10 has been ex-tended to in-clude

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\T1/pcr/m/n/10 fraction_predicted_as_positive\T1/ptm/m/n/10 , \T1/pcr/m/n/10 fr
action_predicted_as_negative\T1/ptm/m/n/10 ,
[563]
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[][][]\T1/pcr/m/sl/10 datarobot.DatetimePartitioningSpecification[][] \T1/ptm/m
/n/10 at-tribute \T1/ptm/m/it/10 de-fault_to_a_priori\T1/ptm/m/n/10 , dep-re-

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[]\T1/ptm/m/n/10 New class for work-ing with model com-pli-ance doc-u-men-ta-ti
on fea-ture of DataRobot:
[564]
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[]\T1/ptm/m/n/10 Cross se-ries fea-tures can now be cre-ated within time se-rie
s mul-ti-series projects us-ing the

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\T1/pcr/m/n/10 use_cross_series_features \T1/ptm/m/n/10 and \T1/pcr/m/n/10 aggr
egation_type \T1/ptm/m/n/10 at-tributes of the [][]\T1/pcr/m/sl/10 datarobot.

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[][][]\T1/pcr/m/sl/10 Project.upload_dataset[][] \T1/ptm/m/n/10 and [][]\T1/pcr
/m/sl/10 Project.create[][] \T1/ptm/m/n/10 now ac-cept an op-tional pa-ram-e-te
r of

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[]\T1/ptm/m/n/10 New op-tional pa-ram-e-ter \T1/ptm/m/it/10 fall-back_to_parent
_insights \T1/ptm/m/n/10 has been added to [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 Training pre-dic-tions for date-time par-ti-tioned projects no
w sup-port the new data sub-set
[565]
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[]\T1/ptm/m/n/10 New meth-ods and ob-jects, in-clud-ing [][]\T1/pcr/m/sl/10 Mod
el.start_advanced_tuning_session[][]\T1/ptm/m/n/10 ,

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[][]\T1/pcr/m/sl/10 Model.get_advanced_tuning_parameters[][]\T1/ptm/m/n/10 , []
[]\T1/pcr/m/sl/10 Model.advanced_tune[][]\T1/ptm/m/n/10 , and

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[]\T1/ptm/m/n/10 A cus-tom pre-dic-tion thresh-old may now be con-fig-ured for 
each model via [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 Individual cross val-i-da-tion scores can be re-trieved for ne
w mod-els us-ing [][]\T1/pcr/m/sl/10 Model.
[566]
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[]\T1/ptm/m/n/10 A new rec-om-men-da-tion type “Rec-om-mended for De-ploy-men
-t” has been added to
[567] [568]
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[]\T1/ptm/m/n/10 Three new op-tions have been made avail-able in the [][]\T1/pc
r/m/sl/10 datarobot.
[569] [570] [571] [572] [573] [574] [575]
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[]\T1/ptm/m/n/10 its
[576] [577]
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[]\T1/pcr/m/n/10 Project.update \T1/ptm/m/n/10 dep-re-cated in fa-vor of spe-ci
fic up-dates: \T1/pcr/m/n/10 rename\T1/ptm/m/n/10 , \T1/pcr/m/n/10 unlock_holdo
ut\T1/ptm/m/n/10 ,
[578] [579]
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[]\T1/pcr/m/n/10 recommendation_settings \T1/ptm/m/n/10 pa-ram-e-ter of \T1/pcr
/m/n/10 Project.start \T1/ptm/m/n/10 is dep-re-cated in fa-vor of
[580] [581] [582] [583] [584]
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[]\T1/pcr/m/n/10 datarobot.models.training_predictions\T1/ptm/m/n/10 ,
[585] (./datarobot-public-api-client.ind [586]
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[]\T1/pcr/m/n/10 AccuracyOverTimePlot \T1/ptm/m/n/10 (\T1/ptm/m/it/10 class in

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\T1/ptm/m/it/10 datarobot.models.datetime_trend_plots\T1/ptm/m/n/10 ),

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[]\T1/pcr/m/n/10 AccuracyOverTimePlotPreview \T1/ptm/m/n/10 (\T1/ptm/m/it/10 cl
ass in

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[]\T1/pcr/m/n/10 AccuracyOverTimePlotsMetadata \T1/ptm/m/n/10 (\T1/ptm/m/it/10 
class in

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[]\T1/pcr/m/n/10 advanced_tune() \T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.mode
ls.Model

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[]\T1/pcr/m/n/10 advanced_tune() \T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.mode
ls.PrimeModel

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[]\T1/pcr/m/n/10 advanced_tune() \T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.mode
ls.RatingTableModel

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[]\T1/pcr/m/n/10 AdvancedTuningSession \T1/ptm/m/n/10 (\T1/ptm/m/it/10 class in


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[]\T1/pcr/m/n/10 AnomalyAssessmentExplanations \T1/ptm/m/n/10 (\T1/ptm/m/it/10 
class in

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[]\T1/pcr/m/n/10 AnomalyAssessmentPredictionsPreview

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[]\T1/pcr/m/n/10 AnomalyAssessmentRecord \T1/ptm/m/n/10 (\T1/ptm/m/it/10 class 
in

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\T1/ptm/m/it/10 datarobot.models.anomaly_assessment\T1/ptm/m/n/10 ),

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[]\T1/pcr/m/n/10 AnomalyOverTimePlot \T1/ptm/m/n/10 (\T1/ptm/m/it/10 class in

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\T1/ptm/m/it/10 datarobot.models.datetime_trend_plots\T1/ptm/m/n/10 ),

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ss in

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[]\T1/pcr/m/n/10 AnomalyOverTimePlotsMetadata \T1/ptm/m/n/10 (\T1/ptm/m/it/10 c
lass in

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s.PairwiseConditionalProbabilities

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[]\T1/pcr/m/n/10 Backtest \T1/ptm/m/n/10 (\T1/ptm/m/it/10 class in datarobot.he
lpers.partitioning_methods\T1/ptm/m/n/10 ),

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.ServiceStatsOverTime

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[]\T1/pcr/m/n/10 calculate_feature_impact()

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[587]
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[]\T1/pcr/m/n/10 calculate_feature_impact()

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[]\T1/pcr/m/n/10 calculate_prediction_intervals()

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VersionDependencyBuild

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ureImpactJob

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ningPredictionsJob

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dels.Project

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[]\T1/pcr/m/n/10 clone_project() \T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.mode
ls.Project

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[]\T1/pcr/m/n/10 clone_user_blueprint()

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[]\T1/pcr/m/n/10 clusters \T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.models.Clus
teringModel at-

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e_project_settings \T1/ptm/m/n/10 to \T1/pcr/m/n/10 True \T1/ptm/m/n/10 in call
s to [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 For date-time par-ti-tioned model you can re-trieve the Ac-cu-
racy over Time plot. To do so use
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[][][]\T1/pcr/m/sl/10 datarobot.FeatureSettings[][] \T1/ptm/m/n/10 con-struc-to
r re-ceives \T1/ptm/m/it/10 fea-ture_name \T1/ptm/m/n/10 and set-tings. For now
 set-tings

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[]\T1/ptm/m/n/10 Enough rows of his-tor-i-cal data must be pro-vided to cover t
he span of the ef-fec-tive Fea-ture

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\T1/ptm/m/n/10 Deriva-tion Win-dow (which may be longer than the pro-jec-t’s 
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\T1/ptm/m/n/10 model can be checked via the \T1/pcr/m/n/10 effective_feature_de
rivation_window_start \T1/ptm/m/n/10 and
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[]\T1/ptm/m/n/10 Prediction in-ter-vals can be cal-cu-lated for a [][]\T1/pcr/m
/sl/10 DatetimeModel[][] \T1/ptm/m/n/10 us-ing the [][]\T1/pcr/m/sl/10 Datetime
Model.
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[]\T1/ptm/m/n/10 Prediction in-ter-vals are also sup-ported for time se-ries mo
del ex-port. See the op-tional
[50] [51] [52] [53] [54 <./sample1.png> <./sample2.png>] [55] [56 <./activation
_map1.png> <./activation_map2.png>]
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[] 
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[]\T1/ptm/m/n/10 Upload a pre-dic-tion dataset that con-tains la-bel col-umn(s)
, re-quest com-pute ex-ter-nal test on one of
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[]\T1/ptm/m/it/10 id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pro-cesses=None\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 project_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sam-ple_pct=Non
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\T1/ptm/m/it/10 ing_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_type=No
ne\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_category=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 is_frozen=N
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\T1/ptm/m/it/10 blueprint_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-rics=None
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\T1/ptm/m/it/10 project=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 data=None\T1/ptm/m
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\T1/ptm/m/it/10 use_project_settings=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sup-
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[]|\T1/ptm/m/n/10 Graphical rep-re-sen-ta-tion

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[]|\T1/ptm/m/n/10 Graphical rep-re-sen-ta-tion

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\T1/ptm/m/n/10 | Data >> Show Ad-

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[]

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[]

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[]|\T1/ptm/m/n/10 {{ con-fu-

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[]|\T1/ptm/m/n/10 Confusion ma-trix sum-
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[]\T1/ptm/m/n/10 Upload a pre-dic-tion dataset that con-tains the tar-get col-u
mn \T1/pcr/m/n/10 PredictionDataset.
[89] [90] [91] [92] [93] [94] [95] [96] [97] [98]
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[]\T1/ptm/m/n/10 If you just want to get gen-er-ated pre-dic-tions from a Pre-d
ic-tJob, you can use the \T1/pcr/m/n/10 PredictJob.
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[]\T1/ptm/m/n/10 The method for cre-at-ing job def-i-ni-tions is iden-ti-cal to
 the ex-ist-ing [][]\T1/pcr/m/sl/10 datarobot.models.
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[]\T1/ptm/m/n/10 Alternatively, you can use a short-hand ver-sion with [][]\T1/
pcr/m/sl/10 datarobot.models.
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[]\T1/ptm/m/n/10 Before ini-ti-at-ing the model re-place-ment re-quest, it is u
su-ally a good idea to use the
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[]\T1/ptm/m/n/10 Refer to [][]\T1/pcr/m/sl/10 ExecutionEnvironment[][] \T1/ptm/
m/n/10 for prop-er-ties of the ex-e-cu-tion en-vi-ron-ment ob-ject and
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[]\T1/ptm/m/it/10 weights=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-sponse_cap=No
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\T1/ptm/m/it/10 out_modeling_mode=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 events_c
ount=None\T1/ptm/m/n/10 ,

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con-

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\T1/ptm/m/n/10 fies the be-hav-ior of Sca-le-out mod-els for the project. This 
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\T1/pcr/m/n/10 datarobot.enums.SCALEOUT_MODELING_MODE\T1/ptm/m/n/10 . If \T1/pc
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\T1/pcr/m/n/10 predictionBalance\T1/ptm/m/n/10 , \T1/pcr/m/n/10 trueFavorableAn
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n/10 , \T1/ptm/m/it/10 back-test=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 source=No
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[]\T1/ptm/m/it/10 deployment\T1/ptm/m/n/10 , \T1/ptm/m/it/10 source_url\T1/ptm/
m/n/10 , \T1/ptm/m/it/10 des-ti-na-tion_url\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre
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[]\T1/ptm/m/n/10 [string] The URL for the pre-dic-tion dataset (e.g.:

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[]\T1/ptm/m/n/10 [string] The URL for the scored dataset (e.g.:
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[]\T1/ptm/m/n/10 [string] The URL for the pre-dic-tion dataset (e.g.:

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[]\T1/ptm/m/n/10 [str or None] Type of au-to-mated doc-u-ment. You can spec-ify
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\T1/ptm/m/n/10 A [][]\T1/ptm/m/it/10 com-pli-ance doc-u-men-ta-tion tem-plate[]
[]\T1/ptm/m/n/10 . Tem-plates are used to cus-tomize con-tents of
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[]\T1/pcr/m/n/10 [ [ True Negative, False Positive ], [ False Negative, True
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[]\T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/ptm/m/it/10 aws_access_key_id=None\T
1/ptm/m/n/10 , \T1/ptm/m/it/10 aws_secret_access_key=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 custom_model_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cus-tom_model
_version_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 en-vi-ron-ment_id\T1/ptm/m/n/10 , \
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odel_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cus-

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-ron-ment_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 en-vi-ron-
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[]\T1/ptm/m/n/10 target type of the cus-tom in-fer-ence model.

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\T1/ptm/m/it/10 datarobot.TARGET_TYPE.MULTICLASS\T1/ptm/m/n/10 , \T1/ptm/m/it/1
0 datarobot.TARGET_TYPE.UNSTRUCTURED\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/n/10 Target fea-ture name; it is op-tional(ignored if pro-vided)

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[]\T1/ptm/m/n/10 Determines
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[]\T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get_type\T1/ptm/m/n/
10 , \T1/ptm/m/it/10 tar-get_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 lan-guag
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l=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 neg-a-tive_class_label=None\T1/ptm/m/n/1
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ss_labels=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 class_labels_file=None\T1/ptm/m/
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\T1/ptm/m/n/10 tom in-fer-ence model. Val-ues: [\T1/ptm/m/it/10 datarobot.TARGE
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tm/m/n/10 , \T1/ptm/m/it/10 de-scrip-tion=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 
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e_class_label=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_threshold=None\
T1/ptm/m/n/10 ,
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[]\T1/ptm/m/n/10 the type of the im-age, ei-ther CUS-TOM_MODEL_IMAGE_TYPE.CUSTO
M_MODEL_IMAGE

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s model or CUS-

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[]\T1/ptm/m/it/10 custom_model_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cus-tom_model
_version_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id=None\T1/ptm/m/n/10 , \T1
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\T1/ptm/m/n/10 “-file1.txt”), (“/home/user/Documents/myModel/folder/file2
.txt”,

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\T1/ptm/m/n/10 “-folder/file2.txt”)] or [“/home/user/Documents/myModel/fi
le1.txt”,

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[]\T1/ptm/m/n/10 Additional pa-ram-e-ters re-

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[]\T1/ptm/m/it/10 custom_model_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 base_environm
ent_id\T1/ptm/m/n/10 ,

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ta=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-
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le1.txt”,

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[]\T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/ptm/m/it/10 de-scrip-tion=None\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 pro-gram-ming_language=None\T1/ptm/m/n/10 , \T1/ptm/
m/it/10 re-
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[]\T1/ptm/m/it/10 execution_environment_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dock
er_context_path\T1/ptm/m/n/10 , \T1/ptm/m/it/10 la-bel=None\T1/ptm/m/n/10 , \T1
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\T1/ptm/m/n/10 See datarobot.enums.EXECUTION_ENVIRONMENT_VERSION_BUILD_STATUS
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\T1/ptm/m/n/10 tween 0 and 1 by DR. only ap-plies to cus-tom es-ti-ma-tors with
 tar-get type
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[]\T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get_type\T1/ptm/m/n/
10 , \T1/ptm/m/it/10 lan-guage=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 de-scrip-ti
on=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cal-i-

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t value

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\T1/ptm/m/n/10 from DR app (True). only ap-plies to cus-tom es-ti-ma-tors with 
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[]\T1/ptm/m/it/10 id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-ator=None\T1/ptm/
m/n/10 , \T1/ptm/m/it/10 base_names=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 class_
name=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/n/10 [str] the full JDBC url, for ex-am-ple \T1/ptm/m/it/10 jdbc:po
stgresql://my.dbaddress.org:5432/my_db\T1/ptm/m/n/10 . 

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[]\T1/ptm/m/n/10 [str] op-tional, the full JDBC url, for ex-am-ple
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[]\T1/ptm/m/it/10 data_source_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 data_sour
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ptm/m/n/10 , \T1/ptm/m/it/10 up-dated=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 para
ms=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 username=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pass-word=None\
T1/ptm/m/n/10 , \T1/ptm/m/it/10 do_snapshot=None\T1/ptm/m/n/10 , \T1/ptm/m/it/1
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\T1/ptm/m/it/10 sist_data_after_ingestion=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 
cat-e-gories=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-den-tial_id=None\T1/ptm/m
/n/10 ,
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[]\T1/ptm/m/it/10 dataset_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ver-sion_id\T1/ptm
/m/n/10 , \T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cat-e-gories\T1/
ptm/m/n/10 , \T1/ptm/m/it/10 cre-ated_at\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-at
ed_by\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 file_path=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 file-like=None
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cat-e-gories=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 data_frame=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 records=None\
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\T1/ptm/m/it/10 e-gories=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 read_timeout=600\
T1/ptm/m/n/10 ,
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tm/m/n/10 , \T1/ptm/m/it/10 per-
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[]\T1/ptm/m/it/10 generator_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id=None\
T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 offset=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 limit=None\T1/ptm
/m/n/10 , \T1/ptm/m/it/10 cat-e-gory=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 or-de
r_by=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fil-
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[]\T1/ptm/m/it/10 project_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 user=None\T
1/ptm/m/n/10 , \T1/ptm/m/it/10 pass-word=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 c
re-den-tial_id=None\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 dataset_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ver-sion_id\T1/ptm
/m/n/10 , \T1/ptm/m/it/10 cat-e-gories\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-ated
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jdbc : DATA _
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[]\T1/ptm/m/n/10 [list(dict)] List of cor-re-la-tion val-ues with a schema de-s
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[]\T1/ptm/m/n/10 [list(dict)] List of joint prob-a-bil-ity val-ues with a schem
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[]\T1/ptm/m/n/10 You might find it more con-ve-nient to use [][]\T1/pcr/m/sl/10
 Feature.get_pairwise_conditional_probabilities[][]
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[]\T1/ptm/m/n/10 E.g. The prob-a-bil-ity P(A=0|B=1) can be re-trieved via: \T1/
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[]\T1/ptm/m/n/10 Each el-e-ment is a \T1/pcr/m/n/10 datarobot.

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[]\T1/ptm/m/n/10 The re-quested re-la-tion-ships

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[]\T1/ptm/m/n/10 List of sec-ondary datasets

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n/10 , \T1/ptm/m/it/10 source\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-ture_fit\T1/p
tm/m/n/10 , \T1/ptm/m/it/10 back-
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m/m/n/10 , \T1/ptm/m/it/10 fea-turelist_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/
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\T1/ptm/m/it/10 turelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id=Non
e\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sam-ple_pct=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-
ing_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-ing_end_date=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_type=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_category=
None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 is_frozen=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 blueprint_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-rics=None
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\T1/ptm/m/it/10 data=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_increasing
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\T1/ptm/m/it/10 n_clusters=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 has_empty_clust
ers=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sup-

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\T1/ptm/m/it/10 ports_monotonic_constraints=None\T1/ptm/m/n/10 , \T1/ptm/m/it/1
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\T1/ptm/m/it/10 tion_threshold=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tio
n_threshold_read_only=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_number=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 par-ent_model
_id=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 sample_pct=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_
id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 scor-ing_type=None\T1/ptm/m/n/10 , \T1/
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\T1/ptm/m/it/10 ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_i
ncreasing_featurelist_id=<object ob-ject>\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-


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\T1/pcr/m/n/10 SCORING_TYPE.cross_validation\T1/ptm/m/n/10 . \T1/pcr/m/n/10 SCO
RING_TYPE.validation \T1/ptm/m/n/10 is
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\T1/ptm/m/n/10 used to train the model. If spec-i-fied, nei-ther \T1/pcr/m/n/10
 training_duration \T1/ptm/m/n/10 nor
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ast_point=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tions_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-t
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\T1/ptm/m/it/10 tual_value_column=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-n
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
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[]\T1/ptm/m/it/10 row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 top_n_features
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\T1/ptm/m/it/10 tures=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 row_count=None\T1/pt
m/m/n/10 ,
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[]\T1/ptm/m/it/10 training_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
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[]\T1/ptm/m/it/10 data_subset\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-na-tion_al
gorithm=None\T1/ptm/m/n/10 ,
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\T1/ptm/m/it/10 turelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id=Non
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\T1/ptm/m/it/10 train-ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-
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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
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\T1/ptm/m/it/10 score=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_increasin
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\T1/ptm/m/it/10 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_th
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\T1/ptm/m/it/10 tion_threshold_read_only=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 m
odel_number=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 source\T1/ptm/m/n/10 , \T1/ptm/m/it/10 top_n_features=None\T1
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\T1/ptm/m/it/10 tures=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 row_count=None\T1/pt
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
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[]\T1/ptm/m/it/10 row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 top_n_features
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\T1/ptm/m/it/10 tion_intervals_size=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-c
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\T1/ptm/m/it/10 tions_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-t
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\T1/ptm/m/it/10 tual_value_column=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-n
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[]\T1/ptm/m/it/10 data_subset\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-na-tion_al
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\T1/ptm/m/it/10 train-ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-
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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
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\T1/ptm/m/it/10 model_type=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_category=
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\T1/ptm/m/it/10 is_frozen=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 blueprint_id=Non
e\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-

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\T1/ptm/m/it/10 mono-tonic_decreasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 sup-ports_monotonic_constraints=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_th
reshold=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_number=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 par-ent_model
_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sup-
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[]\T1/ptm/m/it/10 source\T1/ptm/m/n/10 , \T1/ptm/m/it/10 top_n_features=None\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 fea-

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m/m/n/10 ,
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
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[]\T1/ptm/m/it/10 row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 top_n_features
=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-
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[]\T1/ptm/m/it/10 training_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-ing_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-ing_end_date=None\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 dataset_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 in-clude_predictio
n_intervals=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tion_intervals_size=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-c
ast_point=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tions_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-t
ions_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ac-

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\T1/ptm/m/it/10 tual_value_column=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-n
a-tion_algorithm=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
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[]\T1/ptm/m/it/10 data_subset\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-na-tion_al
gorithm=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 sample_pct=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_
id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 scor-ing_type=None\T1/ptm/m/n/10 , \T1/
ptm/m/it/10 train-

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\T1/ptm/m/it/10 ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_i
ncreasing_featurelist_id=<object ob-ject>\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-

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[]\T1/ptm/m/n/10 [str, op-tional] Ei-ther \T1/pcr/m/n/10 SCORING_TYPE.validatio
n \T1/ptm/m/n/10 or

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\T1/pcr/m/n/10 SCORING_TYPE.cross_validation\T1/ptm/m/n/10 . \T1/pcr/m/n/10 SCO
RING_TYPE.validation \T1/ptm/m/n/10 is

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[]\T1/ptm/m/it/10 featurelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing
_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_duration=None\T1/ptm/
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o-tonic_increasing_featurelist_id=<object

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\T1/ptm/m/it/10 ob-ject>\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_decreasing_
featurelist_id=<object ob-ject>\T1/ptm/m/n/10 ,

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 training_duration \T1/ptm/m/n/10 nor

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\T1/ptm/m/it/10 fea-turelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id
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\T1/ptm/m/it/10 ple_pct=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_row_coun
t=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-

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\T1/ptm/m/it/10 sam-pling_method=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_typ
e=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_category=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 is_frozen=N
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\T1/ptm/m/it/10 blueprint_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-rics=None
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\T1/ptm/m/it/10 data_selection_method=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 back
-tests=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mono-tonic_increasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mono-tonic_decreasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 sup-ports_monotonic_constraints=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_th
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[]\T1/ptm/m/n/10 [str or None] (New in v2.23) in-di-cates the way train-ing dat
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ea-turelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_row_count=None\T1
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ng_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_end_date=None\T1/p
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[]\T1/ptm/m/n/10 [AccuracyOverTimePlotsMetadata] a [][]\T1/pcr/m/sl/10 Accuracy
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
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[]\T1/ptm/m/it/10 training_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
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[]\T1/ptm/m/it/10 featurelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing
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\T1/ptm/m/it/10 time_window_sample_pct=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mon
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\T1/ptm/m/it/10 train-ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-
ing_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-ing_end_date=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_type=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_category=
None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_frozen=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 blueprint_id=Non
e\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-

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\T1/ptm/m/it/10 rics=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 par-ent_model_id=None
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it/10 mono-

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\T1/ptm/m/it/10 tonic_decreasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 sup-ports_monotonic_constraints=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_th
reshold=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tion_threshold_read_only=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 m
odel_number=None\T1/ptm/m/n/10 ,
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\T1/ptm/m/it/10 fea-turelist_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_
name=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id=None
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ver-

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\T1/ptm/m/it/10 sion=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 note=None\T1/ptm/m/n/
10 , \T1/ptm/m/it/10 ori-gin_url=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 im-

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\T1/ptm/m/it/10 ported_by_username=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project
_name=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 cre-ated_by_username=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-a
ted_by_id=None\T1/ptm/m/n/10 ,
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m/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 turelist_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist
_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 project_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sam-ple_pct=Non
e\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-

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\T1/ptm/m/it/10 ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_du
ration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 ing_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_type=No
ne\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_category=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 is_frozen=N
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\T1/ptm/m/it/10 blueprint_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-rics=None
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\T1/ptm/m/it/10 sup-ports_monotonic_constraints=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_th
reshold=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_number=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sup-
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[]\T1/ptm/m/it/10 source\T1/ptm/m/n/10 , \T1/ptm/m/it/10 top_n_features=None\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 tures=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 row_count=None\T1/pt
m/m/n/10 ,
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from

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[]\T1/ptm/m/it/10 row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 top_n_features
=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-
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[]\T1/ptm/m/it/10 training_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-ing_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-ing_end_date=None\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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[]\T1/ptm/m/it/10 dataset_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 in-clude_predictio
n_intervals=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tion_intervals_size=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-c
ast_point=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tions_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-t
ions_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ac-

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\T1/ptm/m/it/10 tual_value_column=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-n
a-tion_algorithm=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
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[]\T1/ptm/m/it/10 data_subset\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-na-tion_al
gorithm=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 sample_pct=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_
id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 scor-ing_type=None\T1/ptm/m/n/10 , \T1/
ptm/m/it/10 train-

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\T1/ptm/m/it/10 ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_i
ncreasing_featurelist_id=<object ob-ject>\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-

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[]\T1/ptm/m/n/10 [str, op-tional] Ei-ther \T1/pcr/m/n/10 SCORING_TYPE.validatio
n \T1/ptm/m/n/10 or

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\T1/pcr/m/n/10 SCORING_TYPE.cross_validation\T1/ptm/m/n/10 . \T1/pcr/m/n/10 SCO
RING_TYPE.validation \T1/ptm/m/n/10 is

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[]\T1/ptm/m/it/10 featurelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing
_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_duration=None\T1/ptm/
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\T1/ptm/m/it/10 time_window_sample_pct=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mon
o-tonic_increasing_featurelist_id=<object

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\T1/ptm/m/it/10 ob-ject>\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_decreasing_
featurelist_id=<object ob-ject>\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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\T1/ptm/m/n/10 used to train the model. If spec-i-fied, nei-ther \T1/pcr/m/n/10
 training_duration \T1/ptm/m/n/10 nor
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[]\T1/ptm/m/n/10 [dict] “-Pa-ram-e-ter-s” dic-tio-nary, same as spec-i-fied
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[]\T1/ptm/m/it/10 eureqa_solution_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 com-plex-i
ty\T1/ptm/m/n/10 , \T1/ptm/m/it/10 er-

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\T1/ptm/m/it/10 ror\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pres-sion\T1/ptm/m/n/10 
, \T1/ptm/m/it/10 ex-pres-sion_annotated\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 user_partition_col\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_
level\T1/ptm/m/n/10 , \T1/ptm/m/it/10 val-i-da-tion_level\T1/ptm/m/n/10 , \T1/p
tm/m/it/10 hold-out_level\T1/ptm/m/n/10 ,
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\T1/ptm/m/it/10 lot_data_selection_method=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 val-i-da-tion_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 hold-out_start_date=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 hold-out_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 dis-able_holdout=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 gap_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 num-

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\T1/ptm/m/it/10 ber_of_backtests=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 back-tests=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 use_time_series=False\T1/ptm/m/n/10 , \T1/ptm/m/it/10 de-

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\T1/ptm/m/it/10 de-fault_to_do_not_derive=False\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 fore-cast_window_end=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 win-dows_basis_unit=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 treat_as_exponential=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 dif-fer-enc-ing_method=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_splits=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 al-

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\T1/ptm/m/it/10 low_partial_history_time_series_predictions=False\T1/pcr/m/n/10
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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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[]\T1/ptm/m/n/10 Note that ei-ther (\T1/pcr/m/n/10 holdout_start_date\T1/ptm/m/
n/10 , \T1/pcr/m/n/10 holdout_duration\T1/ptm/m/n/10 ) or (\T1/pcr/m/n/10 holdo
ut_start_date\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/n/10 [str] one of \T1/pcr/m/n/10 datarobot.enums.

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\T1/pcr/m/n/10 DATETIME_AUTOPILOT_DATA_SELECTION_METHOD\T1/ptm/m/n/10 . Whether
 mod-els cre-
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[]\T1/ptm/m/n/10 [datetime.datetime or None] The start date of hold-out scor-in
g

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[]\T1/ptm/m/n/10 [str or None] The du-ra-tion of the hold-out scor-ing data. If

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[]\T1/ptm/m/n/10 [list of Pe-ri-od-ic-ity, op-tional] (New in ver-sion v2.9) a 
list of

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[]\T1/ptm/m/n/10 (New in ver-sion v2.24)

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[]\T1/ptm/m/it/10 index\T1/ptm/m/n/10 , \T1/ptm/m/it/10 gap_duration=None\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 val-i-da-

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\T1/ptm/m/it/10 tion_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 val-i-da-t
ion_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 val-i-da-tion_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pri
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\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string s
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[]\T1/ptm/m/n/10 Option 2 (New in ver-sion v2.20): Use \T1/pcr/m/n/10 index\T1/
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ion_start_date\T1/ptm/m/n/10 , and \T1/pcr/m/n/10 validation_end_date\T1/ptm/m/
n/10 .

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[]\T1/ptm/m/it/10 feature_name\T1/ptm/m/n/10 , \T1/ptm/m/it/10 known_in_advance
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[]\T1/ptm/m/it/10 project_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 date-time_par
tition_column=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 out_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 hold-out_du
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\T1/ptm/m/it/10 hold-out_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 hold-ou
t_end_date=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 num-ber_of_backtests=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 back-
tests=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 to-

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\T1/ptm/m/it/10 tal_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 use_time_ser
ies=False\T1/ptm/m/n/10 ,

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ndow_start=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 dows_basis_unit=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 treat_as_e
xponential=None\T1/ptm/m/n/10 ,

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mns=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 dar_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_splits=None
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 al-
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[]\T1/ptm/m/n/10 [str] one of \T1/pcr/m/n/10 datarobot.enums.

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\T1/pcr/m/n/10 DATETIME_AUTOPILOT_DATA_SELECTION_METHOD\T1/ptm/m/n/10 . Whether
 mod-els cre-ated
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[]\T1/ptm/m/n/10 [list of Pe-ri-od-ic-ity, op-tional] (New in ver-sion v2.9) a 
list of
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[]\T1/ptm/m/n/10 (New in ver-sion v2.24)
[449] [450] [451]
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\T1/pcr/m/n/10 primary_training_end_date\T1/ptm/m/n/10 , \T1/pcr/m/n/10 validat
ion_start_date\T1/ptm/m/n/10 ,

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\T1/ptm/m/n/10 and \T1/pcr/m/n/10 validation_duration\T1/ptm/m/n/10 ). If \T1/p
cr/m/n/10 True\T1/ptm/m/n/10 , will use a start/end date ap-

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\T1/ptm/m/n/10 proach for spec-i-fy-ing back-tests (\T1/pcr/m/n/10 primary_trai
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 name\T1/ptm/m/n/10
 , \T1/ptm/m/it/10 true_positive_value=1\T1/ptm/m/n/10 , \T1/ptm/m/it/10 true_n
egative_value=1\T1/ptm/m/n/10 ,
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 \T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/ptm/m/it/10 true_positive_value\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 true_negative_value\T1/ptm/m/n/10 ,
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\T1/ptm/b/n/10 Note:  \T1/ptm/m/n/10 Dep-re-cated in v2.3 in fa-vor of \T1/pcr/
m/n/10 Project.upload_dataset \T1/ptm/m/n/10 and \T1/pcr/m/n/10 Model.

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[]\T1/ptm/m/n/10 [str] id of cre-ated job, can be used as pa-ram-e-ter to

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\T1/pcr/m/n/10 PredictJob.get \T1/ptm/m/n/10 or \T1/pcr/m/n/10 PredictJob.get_p
redictions \T1/ptm/m/n/10 meth-ods or
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 id\T1/ptm/m/n/10 ,
 \T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-ated\T1/ptm/m/n/10 , 
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\T1/ptm/m/it/10 num_columns\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_point=Non
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\T1/ptm/m/it/10 con-tains_target_values=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 se
c-
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\T1/ptm/m/it/10 tion_explanations_sample=None\T1/pcr/m/n/10 ) 
[462]
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[]\T1/ptm/m/it/10 id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 ,
 \T1/ptm/m/it/10 model_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id\T1/ptm/m/n
/10 ,

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\T1/ptm/m/it/10 max_explanations\T1/ptm/m/n/10 , \T1/ptm/m/it/10 num_columns\T1
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id\T1/ptm/m/
n/10 , \T1/ptm/m/it/10 dataset_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_explanati
ons=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 thresh-

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[]\T1/ptm/m/n/10 Compute a Pre-dic-tion-Ex-pla-na-tion-sIni-tial-iza-tion for t
he model via \T1/pcr/m/n/10 datarobot.

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[]\T1/ptm/m/n/10 Compute pre-dic-tions for the model and dataset via \T1/pcr/m/
n/10 datarobot.Model.
[464] [465]
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[]\T1/ptm/m/it/10 limit=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 off-set=None\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 ex-
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[]\T1/ptm/m/n/10 [list] an ar-ray of dic-tio-nar-ies with a schema de-scribed a
s
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_expla
nations_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 limit=None\T1/ptm/m/n/10 , \T1/ptm/m
/it/10 off-set=0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_id\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 model_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 dataset_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 in-cludes_predi
ction_intervals=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 cast_point=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tions_s
tart_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-

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\T1/ptm/m/it/10 dic-tions_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ac-tual
_value_column=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 ex-pla-na-tion_algorithm=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 m
ax_explanations=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id=None\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 par-ent_model_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 rule-set_id=None\
T1/ptm/m/n/10 , \T1/ptm/m/it/10 lan-guage=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 tar-get=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get_type=None\
T1/ptm/m/n/10 , \T1/ptm/m/it/10 hold-out_unlocked=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 met-ric=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 stage=None\T1/ptm/
m/n/10 , \T1/ptm/m/it/10 par-ti-tion=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pos-i
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\T1/ptm/m/it/10 tive_class=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-ated=None\T
1/ptm/m/n/10 , \T1/ptm/m/it/10 ad-vanced_options=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 rec-om-mender=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_train_pc
t=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 max_train_rows=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sca-le-out_
max_train_pct=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 sca-le-out_max_train_rows=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 
file_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-den-

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\T1/ptm/m/it/10 tials=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-ture_engineering
_prediction_point=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 tion-ships_configuration_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/1
0 project_description=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 sourcedata\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_name='Unti
tled Project'\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_wait=600\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 data_source_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 user-name\T1/p
tm/m/n/10 , \T1/ptm/m/it/10 pass-word\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 dataset_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_version_id
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\T1/ptm/m/it/10 project_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 user=None\T1/
ptm/m/n/10 , \T1/ptm/m/it/10 pass-word=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 cre-den-tial_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 use_kerber
os=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-den-
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[]\T1/ptm/m/it/10 sourcedata\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get=None\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 project_name='Untitled Project'\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 worker_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-ric=None\
T1/ptm/m/n/10 , \T1/ptm/m/it/10 au-topi-lot_on=True\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pos-i-tive_class=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get_t
ype=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 un-su-per-vised_mode=False\T1/ptm/m/n/
10 ,

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\T1/ptm/m/it/10 blend_best_models=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-pare
_model_for_deployment=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 con-

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\T1/ptm/m/it/10 sider_blenders_in_recommendation=None\T1/ptm/m/n/10 , \T1/ptm/m
/it/10 scor-ing_code_only=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 fea-ture_discovery_supervised_feature_reduction=None\T1/ptm/m/n
/10 , \T1/ptm/m/it/10 un-su-per-
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[]\T1/ptm/m/n/10 Compute “All back-test”

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[]\T1/ptm/m/n/10 (New
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[]\T1/ptm/m/n/10 Project.list(search_params={‘project_name’: ‘red’}) >>
>
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\T1/ptm/m/it/10 pos-i-tive_class=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 par-ti-ti
on-ing_method=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_id=None\T1/ptm/
m/n/10 , \T1/ptm/m/it/10 ad-

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\T1/ptm/m/it/10 vanced_options=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_wait=60
0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get_type=None\T1/ptm/m/n/10 , \T1/ptm/m/i
t/10 cre-den-tials=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 fea-ture_engineering_prediction_point=None\T1/ptm/m/n/10 , \T1/
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on-
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[]\T1/ptm/m/n/10 Raised if \T1/pcr/m/n/10 advanced_options\T1/ptm/m/n/10 , \T1/
pcr/m/n/10 partitioning_method \T1/ptm/m/n/10 or
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\T1/ptm/m/it/10 tions_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-t
ions_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_filename=None\T1/ptm
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\T1/ptm/m/it/10 re-lax_known_in_advance_features_check=None\T1/ptm/m/n/10 , \T1
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[]\T1/ptm/m/n/10 Raised if \T1/pcr/m/n/10 forecast_point \T1/ptm/m/n/10 or \T1/
pcr/m/n/10 predictions_start_date \T1/ptm/m/n/10 and

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[]\T1/ptm/m/it/10 data_source_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 user-name\T1/p
tm/m/n/10 , \T1/ptm/m/it/10 pass-word\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 max_wait=600\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_point=No
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\T1/ptm/m/it/10 den-tials=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tions_st
art_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tions_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ac-tual_val
ue_column=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 dataset_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_version_id
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\T1/ptm/m/it/10 max_wait=600\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_point=No
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\T1/ptm/m/it/10 den-tials=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tions_st
art_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tions_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ac-tual_val
ue_column=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sec-
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[]\T1/ptm/m/it/10 featurelist_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mode='auto'\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 blend_best_models=True\T1/ptm/m/n/10 , \T1/ptm/m/
it/10 scor-

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\T1/ptm/m/it/10 ing_code_only=False\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-pare_mo
del_for_deployment=True\T1/ptm/m/n/10 , \T1/ptm/m/it/10 con-

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\T1/ptm/m/it/10 sider_blenders_in_recommendation=False\T1/ptm/m/n/10 , \T1/ptm/
m/it/10 run_leakage_removed_feature_list=True\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 trainable\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sam-ple_pct=None\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it
/10 source_project_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 scor-

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\T1/ptm/m/it/10 ing_type=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_row_cou
nt=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_increasing_featurelist_id=<o
bject

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[]\T1/ptm/m/n/10 [str, op-tional] Ei-ther \T1/pcr/m/n/10 SCORING_TYPE.validatio
n \T1/ptm/m/n/10 or

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\T1/pcr/m/n/10 SCORING_TYPE.cross_validation\T1/ptm/m/n/10 . \T1/pcr/m/n/10 SCO
RING_TYPE.validation \T1/ptm/m/n/10 is
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[]\T1/ptm/m/it/10 blueprint_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_id=
None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_row_count=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 source_
project_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-

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\T1/ptm/m/it/10 tonic_increasing_featurelist_id=<object ob-ject>\T1/ptm/m/n/10 
, \T1/ptm/m/it/10 mono-

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\T1/ptm/m/it/10 tonic_decreasing_featurelist_id=<object ob-ject>\T1/ptm/m/n/10 
, \T1/ptm/m/it/10 use_project_settings=False\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
 such as those re-turned by the
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\T1/ptm/m/n/10 used to train the model. If spec-i-fied, nei-ther \T1/pcr/m/n/10
 training_duration \T1/ptm/m/n/10 nor

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[]\T1/ptm/m/n/10 [str] Cho-sen blend method, one from \T1/pcr/m/n/10 datarobot.
enums.
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[]\T1/ptm/m/n/10 [str] Cho-sen blend method, one from \T1/pcr/m/n/10 datarobot.
enums.

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[]\T1/ptm/m/n/10 [QUEUE_STATUS enum, op-tional] If called with
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[]\T1/ptm/m/n/10 [QUEUE_STATUS enum, op-tional] If called with

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[]\T1/ptm/m/n/10 [QUEUE_STATUS enum, op-tional] If called with
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[]\T1/ptm/m/n/10 This should be VER-BOSITY_LEVEL.SILENT or VER-
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[]\T1/ptm/m/n/10 If value of \T1/pcr/m/n/10 variable_type \T1/ptm/m/n/10 is not
 from \T1/pcr/m/n/10 datarobot.enums.
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[]\T1/ptm/m/it/10 catalog_version_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 date-
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[]\T1/ptm/m/it/10 supported\T1/ptm/m/n/10 , \T1/ptm/m/it/10 rea-
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id\T1/ptm/m/
n/10 , \T1/ptm/m/it/10 rea-
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[]\T1/ptm/m/n/10 Compute a Rea-son-CodesIni-tial-iza-tion for the model via \T1
/pcr/m/n/10 datarobot.

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[]\T1/ptm/m/n/10 Compute pre-dic-tions for the model and dataset via \T1/pcr/m/
n/10 datarobot.Model.
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[]\T1/ptm/m/it/10 row_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion\T1/ptm/m/
n/10 , \T1/ptm/m/it/10 pre-dic-

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1/ptm/m/n/10 , \T1/ptm/m/it/10 limit=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 off-s
et=0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-
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id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 scor-ing_type=None\T1/ptm/m/n/10 , \T1/
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ea-turelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_row_count=None\T1
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ng_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_end_date=None\T1/p
tm/m/n/10 , \T1/ptm/m/it/10 sam-

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-ing_end_date=None\T1/ptm/m/n/10 ,
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\T1/ptm/m/it/10 time_partition_column=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mul-
ti-series_id_columns=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 project_status_error=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 au-to
pi-lot_done=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/n/10 [float] The to-tal of SHAP val-ues for fea-tures be-yond the
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[520] [521] [522]
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[]\T1/ptm/m/it/10 blender\T1/ptm/m/n/10 , \T1/ptm/m/it/10 blueprint_id\T1/ptm/m
/n/10 , \T1/ptm/m/it/10 di-a-gram\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-tures\T1/
ptm/m/n/10 , \T1/ptm/m/it/10 fea-tures_text\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 icons\T1/ptm/m/n/10 , \T1/ptm/m/it/10 in-sights\T1/ptm/m/n/10 ,
 \T1/ptm/m/it/10 model_type\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sup-ported_target_t
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sion_metadata=None\T1/ptm/m/n/10 ,

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ject_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ver-
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[524]
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m/m/n/10 , \T1/ptm/m/it/10 model_type=None\T1/ptm/m/n/10 ,

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None\T1/ptm/m/n/10 ,
[526] [527]
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m/m/n/10 , \T1/ptm/m/it/10 task_parameters\T1/ptm/m/n/10 ,

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1/ptm/m/n/10 , \T1/ptm/m/it/10 off-set=0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 id=Non
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me=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 order_by='-
[529] [530] [531] [532]
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[]\T1/ptm/m/it/10 id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/p
tm/m/it/10 project_id\T1/ptm/m/n/10 ,

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[533]
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[]\T1/ptm/m/it/10 id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/p
tm/m/it/10 project_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 trans-for-ma-tions=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 trans-for-ma-tion_probability=0.0\T1/ptm/m/n/10 , \T1/ptm/m/it/
10 num-ber_of_new_images=1\T1/ptm/m/n/10 , \T1/ptm/m/it/10 trans-for-ma-

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[]\T1/ptm/m/it/10 image_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id\T1/ptm/m/
n/10 ,

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\T1/ptm/m/it/10 height\T1/ptm/m/n/10 , \T1/ptm/m/it/10 width\T1/ptm/m/n/10 , \T
1/ptm/m/it/10 orig-i-

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\T1/ptm/m/it/10 nal_image_id=None\T1/ptm/m/n/10 ,
[534] [535] [536]
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[]\T1/ptm/m/n/10 Addded new pa-ram-e-ter \T1/ptm/m/it/10 up-load_read_timeout \
T1/ptm/m/n/10 to [][]\T1/pcr/m/sl/10 BatchPredictionJob.score[][] \T1/ptm/m/n/1
0 and

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[]\T1/ptm/m/n/10 Added the abil-ity to turn off su-per-vised fea-ture re-duc-ti
on for Time Se-ries projects. Op-tion
[537] [538]
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[]

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[539]
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[]\T1/ptm/m/n/10 Added the abil-ity to re-trieve de-tected er-rors in the po-te
n-tially mul-ti-cat-e-gor-i-cal fea-

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\T1/ptm/m/n/10 ture types that pre-vented the fea-ture to be iden-ti-fied as mu
l-ti-cat-e-gor-i-cal. [][]\T1/pcr/m/sl/10 Project.

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[][][]\T1/pcr/m/sl/10 datarobot.models.restore_discarded_features.DiscardedFeat
uresInfo.
[540]
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[][][]\T1/pcr/m/sl/10 datarobot.models.restore_discarded_features.DiscardedFeat
uresInfo.

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[]\T1/ptm/m/n/10 Added the abil-ity to con-trol class map-ping ag-gre-ga-tion i
n mul-ti-class projects via

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[]\T1/ptm/m/n/10 Added the abil-ity to com-pute and re-trieve Fea-ture Ef-fects
 for a Mul-ti-class model us-ing

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[][]\T1/pcr/m/sl/10 datarobot.models.Model.request_feature_effects_multiclass()
[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 datarobot.

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\T1/pcr/m/sl/10 models.Model.get_feature_effects_multiclass()[][] \T1/ptm/m/n/1
0 or [][]\T1/pcr/m/sl/10 datarobot.models.Model.

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[][]\T1/pcr/m/sl/10 datarobot.models.DatetimeModel.get_feature_effects_multicla
ss()[][] \T1/ptm/m/n/10 or

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[][]\T1/pcr/m/sl/10 datarobot.models.DatetimeModel.get_or_request_feature_effec
ts_multiclass()[][]

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[][][]\T1/pcr/m/sl/10 datarobot.models.Deployment.get_predictions_by_forecast_d
ate_settings()[][] 

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[][][]\T1/pcr/m/sl/10 datarobot.models.Deployment.update_predictions_by_forecas
t_date_settings()[][] 
[541]
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[]\T1/ptm/m/n/10 Added the abil-ity to re-quest a spe-cific model be pre-pared 
for de-ploy-ment us-ing [][]\T1/pcr/m/sl/10 Project.
[542] [543]
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[]\T1/ptm/m/n/10 Try to read Fea-ture Im-pact re-sult if ex-ist-ing jo-bId is N
one in [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 Updated [][]\T1/pcr/m/sl/10 project.start_autopilot[][] \T1/pt
m/m/n/10 to ac-cept the fol-low-ing new GA pa-ram-e-ters

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\T1/ptm/m/n/10 that are al-ready in the pub-lic API: \T1/pcr/m/n/10 consider_bl
enders_in_recommendation\T1/ptm/m/n/10 ,
[544] [545]
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[]\T1/ptm/m/n/10 Added new helper func-tion to projects to re-trieve the rec-om
-mended model. [][]\T1/pcr/m/sl/10 Project.

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[]\T1/ptm/m/n/10 Added \T1/pcr/m/n/10 docker_context_size \T1/ptm/m/n/10 and \T
1/pcr/m/n/10 docker_image_size \T1/ptm/m/n/10 to [][]\T1/pcr/m/sl/10 datarobot.

[546]
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[]\T1/ptm/m/n/10 Arguments \T1/pcr/m/n/10 dataset_definitions \T1/ptm/m/n/10 an
d \T1/pcr/m/n/10 relationships \T1/ptm/m/n/10 now ac-cept [][]\T1/pcr/m/sl/10 D
atasetDefinition[][]

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\T1/pcr/m/n/10 allow_partial_history_time_series_predictions \T1/ptm/m/n/10 at-
tribute of the [][]\T1/pcr/m/sl/10 datarobot.

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[]\T1/ptm/m/n/10 Add meth-ods to re-trieve per-class lift chart data for mul-ti
-l-abel mod-els: [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 Add meth-ods to re-trieve la-bel-wise ROC curves for mul-ti-l-
abel mod-els: [][]\T1/pcr/m/sl/10 Model.

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\T1/pcr/m/sl/10 get_pairwise_correlations[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/
10 Feature.get_pairwise_joint_probabilities[][] \T1/ptm/m/n/10 and

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[]

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[547]
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[]\T1/ptm/m/n/10 Support for down-load-ing fea-ture dis-cov-ery train-ing or pr
e-dic-tion dataset us-ing [][]\T1/pcr/m/sl/10 Project.

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[]\T1/ptm/m/n/10 Added [][]\T1/pcr/m/sl/10 datarobot.models.FeatureAssociationM
atrix[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 datarobot.

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\T1/pcr/m/sl/10 models.FeatureAssociationMatrixDetails[][] \T1/ptm/m/n/10 and [
][]\T1/pcr/m/sl/10 datarobot.models.

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[]\T1/ptm/m/n/10 Remove \T1/pcr/m/n/10 desired_memory \T1/ptm/m/n/10 param from
 the fol-low-ing meth-ods: [][]\T1/pcr/m/sl/10 CustomInferenceModel.

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\T1/pcr/m/sl/10 create[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 CustomModelVersi
on.create_clean[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 CustomModelVersion.crea
te_clean[][]\T1/ptm/m/n/10 ,
[548]
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[]\T1/ptm/m/n/10 All per-class lift chart data is now avail-able for mul-ti-cla
ss mod-els us-ing [][]\T1/pcr/m/sl/10 Model.

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\T1/ptm/m/n/10 class can now use \T1/pcr/m/n/10 datarobot.TARGET_TYPE.ANOMALY \
T1/ptm/m/n/10 for its \T1/pcr/m/n/10 target_type \T1/ptm/m/n/10 pa-ram-e-ter.

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[]

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[]\T1/ptm/m/n/10 Add sup-port for start-ing and re-triev-ing Fea-ture Im-pact i
n-for-ma-tion for [][]\T1/pcr/m/sl/10 datarobot.

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[]

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[]\T1/ptm/m/n/10 Actions ap-plied to in-put data dur-ing au-to-mated fea-ture d
is-cov-ery can now be re-trieved us-ing

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[]

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[549]
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[]\T1/ptm/m/n/10 New \T1/pcr/m/n/10 sampling_method \T1/ptm/m/n/10 param in [][
]\T1/pcr/m/sl/10 Model.train_datetime[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 P
roject.train_datetime[][]\T1/ptm/m/n/10 ,

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[]

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\T1/pcr/m/n/10 featurelist_id\T1/ptm/m/n/10 , \T1/pcr/m/n/10 credentials_ids\T1
/ptm/m/n/10 , \T1/pcr/m/n/10 project_version \T1/ptm/m/n/10 and \T1/pcr/m/n/10 
is_default \T1/ptm/m/n/10 in

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[][][]\T1/pcr/m/sl/10 SecondaryDatasetConfigurations.

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[]\T1/ptm/m/n/10 Class \T1/pcr/m/n/10 FeatureEngineeringGraph \T1/ptm/m/n/10 ha
s been re-moved. Use [][]\T1/pcr/m/sl/10 datarobot.models.

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[]\T1/ptm/m/n/10 for

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[]\T1/ptm/m/n/10 for [][]\T1/pcr/m/sl/10 datarobot.models.

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[]
[550]
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[]\T1/ptm/m/n/10 Added new ar-gu-ment \T1/pcr/m/n/10 base_environment_id \T1/pt
m/m/n/10 to meth-ods [][]\T1/pcr/m/sl/10 CustomModelVersion.

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[]\T1/ptm/m/n/10 New fields \T1/pcr/m/n/10 base_environment_id \T1/ptm/m/n/10 a
nd \T1/pcr/m/n/10 dependencies \T1/ptm/m/n/10 to class [][]\T1/pcr/m/sl/10 data
robot.

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[]\T1/ptm/m/n/10 Added a new method to trig-ger Fea-ture Im-pact cal-cu-la-tion
 for a Cus-tom In-fer-ence Im-age:

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[]\T1/ptm/m/n/10 Added new pa-ram-e-ter \T1/ptm/m/it/10 met-ric \T1/ptm/m/n/10 
to [][]\T1/pcr/m/sl/10 datarobot.models.TargetDrift[][]\T1/ptm/m/n/10 , [][]\T1
/pcr/m/sl/10 datarobot.models.
[551]
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[]\T1/ptm/m/n/10 The de-fault value for the \T1/pcr/m/n/10 mode \T1/ptm/m/n/10 
param in [][]\T1/pcr/m/sl/10 Project.set_target[][] \T1/ptm/m/n/10 has been cha
nged from
[552]
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[]\T1/ptm/m/n/10 Added new ar-gu-ments \T1/pcr/m/n/10 explanation_algorithm \T1
/ptm/m/n/10 and \T1/pcr/m/n/10 max_explanations \T1/ptm/m/n/10 to method

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[][]\T1/pcr/m/sl/10 Model.request_training_predictions[][]\T1/ptm/m/n/10 . New 
fields \T1/pcr/m/n/10 explanation_algorithm\T1/ptm/m/n/10 ,

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\T1/ptm/m/n/10 New fields \T1/pcr/m/n/10 prediction_explanations \T1/ptm/m/n/10
 and \T1/pcr/m/n/10 shap_metadata \T1/ptm/m/n/10 have been added to class

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[][]\T1/pcr/m/sl/10 TrainingPredictionsIterator[][] \T1/ptm/m/n/10 that is re-t
urned by method [][]\T1/pcr/m/sl/10 TrainingPredictions.

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\T1/pcr/m/sl/10 request_predictions[][]\T1/ptm/m/n/10 . New fields \T1/pcr/m/n/
10 explanation_algorithm\T1/ptm/m/n/10 , \T1/pcr/m/n/10 max_explanations

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\T1/ptm/m/n/10 and \T1/pcr/m/n/10 shap_warnings \T1/ptm/m/n/10 have been added 
to class [][]\T1/pcr/m/sl/10 Predictions[][]\T1/ptm/m/n/10 . Method [][]\T1/pcr
/m/sl/10 Predictions.

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[]\T1/ptm/m/n/10 Added \T1/pcr/m/n/10 model_splits \T1/ptm/m/n/10 to [][]\T1/pc
r/m/sl/10 DatetimePartitioningSpecification[][] \T1/ptm/m/n/10 and to

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[][][]\T1/pcr/m/sl/10 Project.upload_dataset[][] \T1/ptm/m/n/10 and [][]\T1/pcr
/m/sl/10 Model.request_predictions[][] \T1/ptm/m/n/10 now ac-cept
[553]
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[]\T1/ptm/m/n/10 New warn-ing is added to \T1/pcr/m/n/10 data_quality_warnings 
\T1/ptm/m/n/10 of [][]\T1/pcr/m/sl/10 datarobot.models.

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[]\T1/ptm/m/n/10 Scores and in-sights on ex-ter-nal test sets can be re-trieved
 us-ing [][]\T1/pcr/m/sl/10 ExternalScores[][]\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/n/10 Deployments now sup-port pre-dic-tions data col-lec-tion that 
en-ables pre-dic-tion re-

Underfull \hbox (badness 10000) in paragraph at lines 49626--49629
\T1/ptm/m/n/10 quests and re-sults to be saved in Pre-dic-tions Data Stor-age. 
See [][]\T1/pcr/m/sl/10 Deployment.

Underfull \hbox (badness 10000) in paragraph at lines 49626--49629
\T1/pcr/m/sl/10 get_predictions_data_collection_settings[][] \T1/ptm/m/n/10 and
 [][]\T1/pcr/m/sl/10 Deployment.

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[]\T1/ptm/m/n/10 Batch Pre-dic-tion jobs now sup-port scor-ing to Azure and Goo
gle Cloud Stor-age with meth-ods

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[]

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[554]
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[]\T1/ptm/m/n/10 Secondary dataset con-fig-u-ra-tion re-trieve and dele-tion is
 eas-ier now though new

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[][]\T1/pcr/m/sl/10 SecondaryDatasetConfigurations.delete[][] \T1/ptm/m/n/10 so
ft deletes a Sec-ondary dataset con-fig-u-ra-tion.

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[]

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[555] [556]
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[]\T1/ptm/m/n/10 A new work-flow is avail-able for mak-ing pre-dic-tions in tim
e se-ries projects. To that end,

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[]\T1/ptm/m/n/10 Per-class lift chart data is now avail-able for mul-ti-class m
od-els us-ing [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 A new boolean at-tribute \T1/pcr/m/n/10 unsupervised_mode \T1/
ptm/m/n/10 was added to [][]\T1/pcr/m/sl/10 datarobot.

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\T1/pcr/m/sl/10 DatetimePartitioningSpecification[][]\T1/ptm/m/n/10 . When it i
s set to True, date-time par-

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\T1/ptm/m/n/10 ti-tion-ing for un-su-per-vised time se-ries projects will be co
n-structed for now-cast-ing:
[557]
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[]\T1/ptm/m/n/10 Time se-ries model de-ploy-ments now sup-port pre-dic-tion in-
ter-vals. See

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[][]\T1/pcr/m/sl/10 Deployment.get_prediction_intervals_settings[][] \T1/ptm/m/
n/10 and [][]\T1/pcr/m/sl/10 Deployment.

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\T1/ptm/m/n/10 a new op-tional pa-ram-e-ter \T1/pcr/m/n/10 prediction_intervals
_size \T1/ptm/m/n/10 has been added to [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 It’s now pos-si-ble to get fea-ture as-so-ci-a-tion sta-tuse
s for fea-turelists us-ing [][]\T1/pcr/m/sl/10 Project.
[558] [559]
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[]\T1/ptm/m/n/10 Monotonic con-straints are now sup-ported for OTV projects. To
 that end, the pa-ram-e-ters

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[]\T1/ptm/m/n/10 The method [][]\T1/pcr/m/sl/10 Model.get_supported_capabilitie
s[][] \T1/ptm/m/n/10 now has an ex-tra field
[560] [561] [562]
Underfull \hbox (badness 10000) in paragraph at lines 50415--50418
[]\T1/ptm/m/n/10 The dataframe re-turned from [][]\T1/pcr/m/sl/10 datarobot.Pre
dictionExplanations.

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[]\T1/ptm/m/n/10 Information re-trieval for [][]\T1/pcr/m/sl/10 ROC Curve[][] \
T1/ptm/m/n/10 has been ex-tended to in-clude

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\T1/pcr/m/n/10 fraction_predicted_as_positive\T1/ptm/m/n/10 , \T1/pcr/m/n/10 fr
action_predicted_as_negative\T1/ptm/m/n/10 ,
[563]
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[][][]\T1/pcr/m/sl/10 datarobot.DatetimePartitioningSpecification[][] \T1/ptm/m
/n/10 at-tribute \T1/ptm/m/it/10 de-fault_to_a_priori\T1/ptm/m/n/10 , dep-re-

Underfull \hbox (badness 10000) in paragraph at lines 50539--50541
[]\T1/ptm/m/n/10 New class for work-ing with model com-pli-ance doc-u-men-ta-ti
on fea-ture of DataRobot:
[564]
Underfull \hbox (badness 10000) in paragraph at lines 50554--50558
[]\T1/ptm/m/n/10 Cross se-ries fea-tures can now be cre-ated within time se-rie
s mul-ti-series projects us-ing the

Underfull \hbox (badness 10000) in paragraph at lines 50554--50558
\T1/pcr/m/n/10 use_cross_series_features \T1/ptm/m/n/10 and \T1/pcr/m/n/10 aggr
egation_type \T1/ptm/m/n/10 at-tributes of the [][]\T1/pcr/m/sl/10 datarobot.

Underfull \hbox (badness 10000) in paragraph at lines 50569--50572
[][][]\T1/pcr/m/sl/10 Project.upload_dataset[][] \T1/ptm/m/n/10 and [][]\T1/pcr
/m/sl/10 Project.create[][] \T1/ptm/m/n/10 now ac-cept an op-tional pa-ram-e-te
r of

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[]\T1/ptm/m/n/10 New op-tional pa-ram-e-ter \T1/ptm/m/it/10 fall-back_to_parent
_insights \T1/ptm/m/n/10 has been added to [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 Training pre-dic-tions for date-time par-ti-tioned projects no
w sup-port the new data sub-set
[565]
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[]\T1/ptm/m/n/10 New meth-ods and ob-jects, in-clud-ing [][]\T1/pcr/m/sl/10 Mod
el.start_advanced_tuning_session[][]\T1/ptm/m/n/10 ,

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[][]\T1/pcr/m/sl/10 Model.get_advanced_tuning_parameters[][]\T1/ptm/m/n/10 , []
[]\T1/pcr/m/sl/10 Model.advanced_tune[][]\T1/ptm/m/n/10 , and

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[]\T1/ptm/m/n/10 A cus-tom pre-dic-tion thresh-old may now be con-fig-ured for 
each model via [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 Individual cross val-i-da-tion scores can be re-trieved for ne
w mod-els us-ing [][]\T1/pcr/m/sl/10 Model.
[566]
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[]\T1/ptm/m/n/10 A new rec-om-men-da-tion type “Rec-om-mended for De-ploy-men
-t” has been added to
[567] [568]
Underfull \hbox (badness 10000) in paragraph at lines 50929--50936
[]\T1/ptm/m/n/10 Three new op-tions have been made avail-able in the [][]\T1/pc
r/m/sl/10 datarobot.
[569] [570] [571] [572] [573] [574] [575]
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[]\T1/ptm/m/n/10 its
[576] [577]
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[]\T1/pcr/m/n/10 Project.update \T1/ptm/m/n/10 dep-re-cated in fa-vor of spe-ci
fic up-dates: \T1/pcr/m/n/10 rename\T1/ptm/m/n/10 , \T1/pcr/m/n/10 unlock_holdo
ut\T1/ptm/m/n/10 ,
[578] [579]
Underfull \hbox (badness 10000) in paragraph at lines 51933--51935
[]\T1/pcr/m/n/10 recommendation_settings \T1/ptm/m/n/10 pa-ram-e-ter of \T1/pcr
/m/n/10 Project.start \T1/ptm/m/n/10 is dep-re-cated in fa-vor of
[580] [581] [582] [583] [584]
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[]\T1/pcr/m/n/10 datarobot.models.training_predictions\T1/ptm/m/n/10 ,
[585] (./datarobot-public-api-client.ind [586]
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[]\T1/pcr/m/n/10 AccuracyOverTimePlot \T1/ptm/m/n/10 (\T1/ptm/m/it/10 class in

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\T1/ptm/m/it/10 datarobot.models.datetime_trend_plots\T1/ptm/m/n/10 ),

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[]\T1/pcr/m/n/10 AccuracyOverTimePlotPreview \T1/ptm/m/n/10 (\T1/ptm/m/it/10 cl
ass in

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[]\T1/pcr/m/n/10 AccuracyOverTimePlotsMetadata \T1/ptm/m/n/10 (\T1/ptm/m/it/10 
class in

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[]\T1/pcr/m/n/10 advanced_tune() \T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.mode
ls.Model

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[]\T1/pcr/m/n/10 advanced_tune() \T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.mode
ls.PrimeModel

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[]\T1/pcr/m/n/10 advanced_tune() \T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.mode
ls.RatingTableModel

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[]\T1/pcr/m/n/10 AdvancedTuningSession \T1/ptm/m/n/10 (\T1/ptm/m/it/10 class in


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[]\T1/pcr/m/n/10 AnomalyAssessmentExplanations \T1/ptm/m/n/10 (\T1/ptm/m/it/10 
class in

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[]\T1/pcr/m/n/10 AnomalyAssessmentPredictionsPreview

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\T1/ptm/m/n/10 (\T1/ptm/m/it/10 class in datarobot.models.anomaly_assessment\T1
/ptm/m/n/10 ),

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[]\T1/pcr/m/n/10 AnomalyAssessmentRecord \T1/ptm/m/n/10 (\T1/ptm/m/it/10 class 
in

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\T1/ptm/m/it/10 datarobot.models.anomaly_assessment\T1/ptm/m/n/10 ),

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[]\T1/pcr/m/n/10 AnomalyOverTimePlot \T1/ptm/m/n/10 (\T1/ptm/m/it/10 class in

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\T1/ptm/m/it/10 datarobot.models.datetime_trend_plots\T1/ptm/m/n/10 ),

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[]\T1/pcr/m/n/10 AnomalyOverTimePlotPreview \T1/ptm/m/n/10 (\T1/ptm/m/it/10 cla
ss in

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\T1/ptm/m/it/10 datarobot.models.datetime_trend_plots\T1/ptm/m/n/10 ),

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[]\T1/pcr/m/n/10 AnomalyOverTimePlotsMetadata \T1/ptm/m/n/10 (\T1/ptm/m/it/10 c
lass in

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[]\T1/pcr/m/n/10 as_dataframe() \T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.model
s.PairwiseConditionalProbabilities

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[]\T1/pcr/m/n/10 as_dataframe() \T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.model
s.PairwiseCorrelations

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[]\T1/pcr/m/n/10 as_dataframe() \T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.model
s.PairwiseJointProbabilities

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[]\T1/pcr/m/n/10 assign_training_data()

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\T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.CustomInferenceModel method\T1/ptm/m/
n/10 ),

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